Systems and methods for measuring tissue oxygenation

ABSTRACT

The disclosure provides methods and systems for determining tissue oxygenation. An electronic device obtains a data set including a plurality of images of a tissue of interest, each resolved at a different spectral band. Spectral analysis is performed, upon image registration, at a plurality of points in a two-dimensional area of the images of the tissue. The spectral analysis including determining approximate values of oxyhemoglobin levels and deoxyhemoglobin levels at each respective point in the plurality of points. The predetermined set of eight to twelve spectral bands includes spectral bands that provide improved methods for measuring tissue oxygenation.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/969,039, filed Mar. 21, 2014, U.S. Provisional Patent ApplicationNo. 62/090,302, filed Dec. 10, 2014, and U.S. Provisional PatentApplication No. 62/090,324, filed Dec. 10, 2014, the disclosures ofwhich are hereby incorporated by reference herein in their entiretiesfor all purposes.

TECHNICAL FIELD

The present disclosure generally relates to spectroscopy, such ashyperspectral or multi-spectral imaging, and in particular, to systems,methods and devices for performing hyperspectral imaging of chromophoresystems.

BACKGROUND

Hyperspectral (also known as “multispectral”) spectroscopy is an imagingtechnique that integrates multiple images of an object resolved atdifferent spectral bands (e.g., ranges of wavelengths) into a singledata structure, referred to as a three-dimensional hyperspectral datacube. Hyperspectral spectroscopy is often used to identify an individualcomponent of a complex composition through the recognition ofcorresponding spectral signatures of the individual components in aparticular hyperspectral data cube.

Hyperspectral spectroscopy has been used in a variety of applications,ranging from geological and agricultural surveying to militarysurveillance and industrial evaluation. Hyperspectral spectroscopy hasalso been used in medical applications to facilitate complex diagnosisand predict treatment outcomes. For example, medical hyperspectralimaging has been used to accurately predict viability and survival oftissue deprived of adequate perfusion, and to differentiate diseased(e.g. tumor) and ischemic tissue from normal tissue.

Despite the great potential clinical value of hyperspectral imaging,however, several drawbacks have limited the use of hyperspectral imagingin the clinic setting. In particular, current medical hyperspectralinstruments are costly because of the complex optics and computationalrequirements currently used to resolve images at a plurality of spectralbands to generate a suitable hyperspectral data cube. Hyperspectralimaging instruments can also suffer from poor temporal and spatialresolution, as well as low optical throughput, due to the complex opticsand taxing computational requirements needed for assembling, processing,and analyzing data into a hyperspectral data cube suitable for medicaluse. Moreover, because hyperspectral imaging is time consuming andrequires complex optical equipment, it is more expensive than theconventional methods.

SUMMARY

Various implementations of systems, methods and devices within the scopeof the appended claims each have several aspects, no single one of whichis solely responsible for the desirable attributes described herein.Without limiting the scope of the appended claims, some prominentfeatures are described herein. After considering this discussion, andparticularly after reading the section entitled “Detailed Description”one will understand how the features of various implementations are usedto enable improved ulcer formation detection.

In one implementation, the disclosure provides methods, devices, andnontransitory computer-readable storage medium for determining tissueoxygenation according to a method. The method includes obtaining a dataset comprising a plurality of images of a tissue of interest. Eachrespective image in the plurality of images is resolved at a differentspectral band, in a predetermined set of eight to twelve spectral bands.Further, each respective image in the plurality of images comprises anarray of pixel values. In the method, the plurality of images areregistered, using the processor, on a pixel-by-pixel basis, to form aplurality of registered images of the tissue. This plurality of imagesis referred to as a composite image or a hypercube. In the method,spectral analysis is performed at a plurality of points in atwo-dimensional area of the plurality of registered images of thetissue. In some instances, the term “point” and “pixel” is synonymous.However, in other instances, each “point” is a predetermined number ofpixels in the two-dimensional area of the plurality of registered imagesof the tissue. For instance, in some embodiments, there is a one-to-manyrelationship between points and pixels, where, for example, (e.g., eachpoint represents two pixels, each point represent three pixels, and soforth). In the method, the spectral analysis includes determiningapproximate values of oxyhemoglobin levels and deoxyhemoglobin levels ateach respective point in the plurality of points.

In some embodiments, the predetermined set of eight to ten spectralbands includes all the spectral bands in the set of {510±3 nm, 530±3 nm,540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm, 620±3 nm, and 660±3 nm}, whereeach respective spectral band in the eight to ten spectral bands has afull width at half maximum of less than 15 nm, less than 10 nm, or 5 nmor less.

In some embodiments, the predetermined set of eight to ten spectralbands includes spectral bands in the set of {520±3 nm, 540±3 nm, 560±3nm, 580±3 nm, 590±3 nm, 610±3 nm, 620±3 nm, and 640±3 nm}, where eachrespective spectral band in the eight to ten spectral bands has a fullwidth at half maximum of less than 15 nm, less than 10 nm, or 5 nm orless.

In some embodiments, the predetermined set of eight to ten spectralbands includes spectral bands in the set of {500±3 nm, 530±3 nm, 545±3nm, 570±3 nm, 585±3 nm, 600±3 nm, 615±3 nm, and 640±3 nm}, where eachrespective spectral band in the eight to ten spectral bands has a fullwidth at half maximum of less than 15 nm, less than 10 nm, or 5 nm orless.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the present disclosure can be understood in greater detail, amore particular description may be had by reference to the features ofvarious implementations, some of which are illustrated in the appendeddrawings. The appended drawings, however, merely illustrate the morepertinent features of the present disclosure and are therefore not to beconsidered limiting, for the description may admit to other effectivefeatures and arrangements.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1A is a schematic example of a distributed diagnostic environmentincluding a hyperspectral imaging device according to someimplementations.

FIG. 1B is a schematic diagram of a local diagnostic environmentaccording to some implementations.

FIG. 2 is a block diagram of an implementation of a hyperspectralimaging device used in accordance with some embodiments of the presentdisclosure.

FIG. 3 is a schematic illustration of a hyperspectral data cube.

FIGS. 4A, 4B and 4C are flow-diagrams illustrating a method of measuringtissue oxygenation according to some implementations.

FIG. 5 is a schematic illustration of the internal hardware of aco-axial hyperspectral/multispectral camera mounted in a housing,according to some implementations. The illustration shows across-section down the barrel of the camera with a perspective view ofthe beam steering element 204.

FIG. 6 is a schematic illustration of the light path for a capturedhyperspectral/multispectral image, according to some implementationsemploying a co-axial hyperspectral imager with a beam-steering element.

FIG. 7 is an exploded schematic view of an implementation of an imagesensor assembly, according to some implementations employing asingle-sensor hyperspectral imager.

FIG. 8 is an exploded schematic view of a multi-sensor simultaneouscapture hyperspectral imaging device, according to some implementations.

FIG. 9 illustrates an example of the output from averaging OXY valuesover the square segmentation of an OXY map, generated according to someimplementations.

FIGS. 10A and 10B illustrate OXY or DEOXY scatter plots for exemplarysubset 5778 of eight wavelenghs, generated according to someimplementations.

FIGS. 11A, 11B, 11C, 11D, and 11E show OXY and DEOXY maps, generatedfrom a first hyperspectral data set, using all fifteen wavelengths (FIG.11A—OXY; FIG. 11C—DEOXY) and those generated using only the eightwavelengths of exemplary subset 5778 (FIG. 11B—OXY; FIG. 11D—DEOXY),according to some implementations. The OXY and DEOXY maps generatedusing only eight wavelengths were corrected using linear correctionfactors for subset number 2. FIG. 11E shows a native image of thetissue.

FIGS. 12A, 12B, 12C, and 12D illustrate statistics generated from thethree OXY and DEOXY maps generated using all fifteen wavelengths or onlythe eight wavelengths of exemplary subset 5778, from the firsthyperspectral data set, according to some implementations. FIGS. 12A and12 C illustrate histograms showing the pixel value distribution of theOXY and DEOXY maps, respectively. FIGS. 12B and 12D illustrate scatterplots of the uncorrected and corrected pixel values determined usingeight wavelengths plotted against pixel values determined using allfifteen wavelengths.

FIGS. 13A, 13B, 13C, and 13D illustrate qualitative analysis of the OXYand DEOXY maps generated from the first data set, using fifteen andeight wavelengths, according to some implementations. FIGS. 13A and 13Cillustrate mean pixel values for approximately 40-pixel squares overlaidon the OXY and DEOXY maps generated using all fifteen wavelengths,respectively. The cross indicates the bottom right of each square. FIGS.13B and 13D illustrate the difference between the averaged values in themaps generated using fifteen wavelengths and the corrected mapsgenerated using the eight wavelengths of exemplary subset 5778, overlaidon OXY and DEOXY maps generated using the corrected eight wavelengths,respectively.

FIGS. 14A, 14B, 14C, 14D, and 14E show OXY and DEOXY maps, generatedfrom a second hyperspectral data set, using all fifteen wavelengths(FIG. 14A—OXY; FIG. 14C—DEOXY) and those generated using only the eightwavelengths of exemplary subset 5778 (FIG. 14B—OXY; FIG. 14D—DEOXY),according to some implementations. The OXY and DEOXY maps generatedusing only eight wavelengths were corrected using the linear correctionfactors for subset number 2. FIG. 14E shows a native image of thetissue.

FIGS. 15A, 15B, 15C, and 15D illustrate statistics generated from thethree OXY and DEOXY maps generated using all fifteen wavelengths or onlythe eight wavelengths of exemplary subset 5778, from the secondhyperspectral data set, according to some implementations. FIGS. 15A and15C illustrate histograms showing the pixel value distribution of theOXY and DEOXY maps, respectively. FIGS. 15B and 15D illustrate scatterplots of the uncorrected and corrected pixel values determined usingeight wavelengths plotted against pixel values determined using allfifteen wavelengths.

FIGS. 16A, 16B, 16C and 16D illustrate qualitative analysis of the OXYand DEOXY maps generated from the second hyperspectral data set, usingfifteen and eight wavelengths, according to some implementations. FIGS.16A and 16C illustrate mean pixel values for approximately 40-pixelsquares overlaid on the OXY and DEOXY maps generated using all fifteenwavelengths, respectively. The cross indicates the bottom right of eachsquare. FIGS. 16B and 16D illustrate the difference between the averagedvalues in the maps generated using fifteen wavelengths and the correctedmaps generated using the eight wavelengths of exemplary subset 5778,overlaid on OXY and DEOXY maps generated using the corrected eightwavelengths, respectively.

FIGS. 17A, 17B, 17C, 17D, and 17E show OXY and DEOXY maps, generatedfrom a third hyperspectral data set, using all fifteen wavelengths (FIG.17A—OXY; FIG. 17C—DEOXY) and those generated using only the eightwavelengths of exemplary subset 5778 (FIG. 17B—OXY; FIG. 17D—DEOXY),according to some implementations. The OXY and DEOXY maps generatedusing only eight wavelengths were corrected using the linear correctionfactors for subset number 2. FIG. 17E shows a native image of thetissue.

FIGS. 18A, 18B, 18C, and 18D illustrate statistics generated from thethree OXY and DEOXY maps generated using all fifteen wavelengths or onlythe eight wavelengths of exemplary subset 5778, from the thirdhyperspectral data set, according to some implementations. FIGS. 18A and18C illustrate histograms showing the pixel value distribution of theOXY and DEOXY maps, respectively. FIGS. 18B and 18D illustrate scatterplots of the uncorrected and corrected pixel values determined usingeight wavelengths plotted against pixel values determined using allfifteen wavelengths.

FIGS. 19A, 19B, 19C, and 19D illustrate qualitative analysis of the OXYand DEOXY maps generated from the third hyperspectral data set, usingfifteen and eight wavelengths, according to some implementations. FIGS.19A and 19C illustrate mean pixel values for approximately 40-pixelsquares overlaid on the OXY and DEOXY maps generated using all fifteenwavelengths, respectively. The cross indicates the bottom right of eachsquare. FIGS. 19B and 19D illustrate the difference between the averagedvalues in the maps generated using fifteen wavelengths and the correctedmaps generated using the eight wavelengths of exemplary subset 5778,overlaid on OXY and DEOXY maps generated using the corrected eightwavelengths, respectively.

DETAILED DESCRIPTION

Numerous details are described herein in order to provide a thoroughunderstanding of the example implementations illustrated in theaccompanying drawings. However, the invention may be practiced withoutmany of the specific details. And, well-known methods, components, andcircuits have not been described in exhaustive detail so as not tounnecessarily obscure more pertinent aspects of the implementationsdescribed herein.

FIGS. 1-3, described below, provide descriptions of exemplary imagingsystems and hyperspectral data cubes for use with the embodimentdescribed herein. FIGS. 4A-4C are flow diagrams illustrating a method ofmeasuring tissue oxygenation.

FIG. 1A is an example of a distributed diagnostic environment 10including an imaging device 100 according to some implementations. Insome implementations, the distributed diagnostic environment 10 includesone or more clinical environments 20, one or more processing and/orstorage centers 50, and a communication network 156 that, together withone or more Internet Service Providers 60 and/or Mobile phone operators40, with concomitant cell towers 42, allow communication between the oneor more clinical environments 20 and the one or more processing and/orstorage centers 50.

The clinical environment 20 depicted in FIG. 1 is designed toaccommodate the demand of many subjects 22, by taking advantage ofimproved hyperspectral imaging techniques described herein. In someimplementations, the clinical environment 20 includes a medicalprofessional 21 operating an imaging device 100 to collect a series ofimages of a subject's 22 tissue. In some embodiments, the clinicalenvironment also includes a communication device 26 that communicateswith processing and/or storage center 50 via communications network 156.In some embodiments, the clinical environment 20 includes a processingdevice 24 for processing hyperspectral images without reliance onprocessing and/or storage center 50. In some embodiments, the clinicalenvironment includes both a communication device 26 and a processingdevice 24.

In some implementations, the imaging device 100 illuminates an object(e.g., an area of the body of a subject 22) and generates imaging dataof the object. In some implementations, the imaging device 100illuminates an object using one or more light sources 120. In someimplementations, after illuminating the object, or concurrently thereto,the imaging device 100 generates and transmits imaging data (e.g., thehyperspectral image data set) corresponding to the object to processingand/or storage center 50 for forming a processed hyperspectral image. Inother implementations, the imaging device 100 and/or processing device24 form the processed hyperspectral image using the image data set, andtransmits the processed hyperspectral image to the processing and/orstorage center 50.

In some implementations, spectral analysis of the imaging data isperformed at the processing and/or storage center 50, e.g., usingprocessing server 52. In some implementations, a record of the spectralanalysis is created in a database 54 at the processing and/or storagecenter 50. In some implementations, a record of the spectral analysisand/or an indication of a physiologic condition based on the spectralanalysis is sent from the processing and/or storage center 50 back tothe clinical environment 20.

In some implementations, image capture and processing includes theimaging device 100 collecting a plurality of images of a region ofinterest of a subject (e.g., a first image captured at a first spectralbandwidth and a second image captured at a second spectral bandwidth).The imaging device 100 stores each respective image at a respectivememory location (e.g., the first image is stored at a first location inmemory 220 and the second image is stored at a second location in memory220). The imaging device 100 compares, on a pixel-by-pixel basis (e.g.,with processor 210), each pixel of the respective images to produce acomposite (e.g., hyperspectral, multi-spectral) image of the region ofinterest of the subject. In some implementations, individual pixelvalues are binned, averaged, or otherwise arithmetically manipulatedprior to pixel-by-pixel analysis, e.g., pixel-by-pixel comparisonincludes comparison of binned, averaged, or otherwise arithmeticallymanipulated pixel values.

In other implementations, spectral analysis is performed at the clinicalenvironment 20, e.g., using the imaging device 100 and/or processingdevice 24. In some implementations, a record of the spectral analysisand/or an indication of a physiologic condition based on the spectralanalysis is then sent from the clinical environment 20 to the processingand/or storage center 50, where a record is created in database 54. Insome implementations, a record of the spectral analysis and/or anindication of a physiologic condition is created at a local database inthe clinical environment 20. In some implementations, the local databaseis in the imaging device 100, allowing for optional transfer later to adifferent local or external database. In other embodiments, the localdatabase is connected wired or wirelessly to the imaging device 100 orprocessing device 24.

In some implementations, a record of the spectral analysis and/or anindication of a physiologic condition is outputted at the clinicalenvironment for examination by a medical professional 21, which may bethe same or different medical professional who operated the imagingdevice. In some implementations, a record of the spectral analysisand/or an indication of a physiologic condition is outputted at anexternal diagnostics environment 70 including a communications device 72in communication with the clinical environment 20 and/or processingand/or storage center 50 via the communication network 156.

In some implementations, the medical professional 21, after examiningthe outputted spectral analysis or indication of a physiologicalcondition, assigns a course of treatment for the subject 22. In someimplementations, the treatment may be administered by the same medicalprofessional 21 who operated the imaging device 100, by the medicalprofessional 21 who reviewed the indication of the physiologicalparameter, by another medical professional 21, or by the subject 22themselves.

FIG. 1B is a schematic diagram of a clinical diagnostic environment 20according to some implementations. The clinical diagnostic environment20 includes an imaging device 100 and a communications module 150. Thecommunications module 150 is used, for example, to optionallycommunicate imaging data to a remote location, to communicate a recordof the imaging analysis and/or an indication of a physiologic condition,and/or to receive software updates or diagnostic information.

In some implementations, the imaging device 100 illuminates an area ofthe body of a subject 22 (e.g., a location on an upper extremity 24 orlocation on a lower extremity 26 of the subject 22) and generatesimaging data of the area. In some implementations, the imaging device100 illuminates the area of the body of the subject using one or morelight sources (120). Such light sources emit light 28 that is reflectedby area 24 to form reflected light 30 that is received by sensor module110. Sensor module 100 includes photo-sensors 112 and filters 114.

In some embodiments, for example, where the imaging device 100 employs aphoto-sensor array coupled to a filter array, the output from thephoto-sensors 112 is sent to registers 142 of an interface module 140and processed by one or more register look-up tables 144 and selectioncircuitry 146. For instance, in some embodiments, look-up table 144 isused in the following manner. In such embodiments, for purposes ofillustration, registers 142 is a plurality of registers. The imagingdevice 100 uses the registers 142 to receive the output of thephoto-sensors 112 and the control module 130 identifies which registers142 in the plurality of registers correspond to filter elements of aparticular filter-type in a plurality of filter-types using the look-uptable. The control module 130 selects one or more subsets ofphoto-sensor outputs from the plurality of registers based on theidentification of the registers that correspond to filter elements ofthe particular filter-type. The independent subsets of photo-sensors arethen used to form independent images, each image corresponding to afilter-type. To this end, in some implementations there is selectioncontrol circuitry 146 to select data using column select and row selectcircuitry. This data is stored and processed in registers 142.

Operation of the light source 120, sensor module 110 and interfacemodule 140 is under the control of control module 130. In someembodiments, as illustrated in FIG. 1B, control module 130, in turn,interacts with a communications module 150 in order to facilitate theacquisition of imaging data from a subject 22.

FIG. 2 is a block diagram of an implementation of an imaging device,such as imaging device 100. In particular, FIG. 2 is not limited to anyparticular configuration of image acquisition modalities, such as thebeam-steering embodiments described with respect to FIGS. 5 and 6, thesingle sensor embodiments described with respect to FIG. 7, and theconcurrent capture on multiple photo-sensors embodiments described withrespect to FIG. 8. In fact, FIG. 2 encompasses any form of imagingdevice provided that enables collection of a spectral image inaccordance with the methods described in more detail below, e.g., inaccordance with the methods described in FIG. 4A-4C.

The methods described herein can be employed with any knownhyperspectral/multispectral imaging system or other form of imagingsystem. For example, in one embodiment, the methods described herein areemployed in conjunction with a spatial scanning HSI system. Spatialscanning HSI systems include point scanning and line-scanning imagingsystems in which a complete spectrum is concurrently acquired at asingle pixel or line of pixels. The instrument then scans through aregion of interest collecting complete spectrums at each point (e.g.,pixel) or line (e.g., line of pixels) sequentially. In anotherembodiment, the methods described herein are employed in conjunctionwith a spectral scanning HSI system. Spectral scanning HSI systemsacquire an image of the entire region of interest at a single wavelengthwith a two-dimensional detector. The instrument collects a series ofimages of the entire region of interest as each wavelength in apredetermined set of wavelengths.

As such, FIG. 2 encompasses a broad range of imaging devices, providedthey are capable of collecting a hyperspectral image series in themanner disclosed herein. As such, FIG. 2 represents, by way of exampleand upon adaption to perform the methods disclosed herein, any of theimaging devices of FIGS. 5 through 8 described below, and/or any of theimaging devices disclosed in International Patent Publication Nos. WO2014/007869, WO 2013/184226, WO 2014/063117, and WO 2014/146053, each ofwhich is hereby incorporated by reference herein in its entirety.

While some example features are illustrated in FIG. 2, those skilled inthe art will appreciate from the present disclosure that various otherfeatures have not been illustrated for the sake of brevity and so as notto obscure more pertinent aspects of the example implementationsdisclosed herein. To that end, the imaging device 100 includes one ormore central processing units (CPU) 210, an optional main non-volatilestorage unit 209, an optional controller 208, a system memory 220 forstoring system control programs, data, and application programs,including programs and data optionally loaded from the non-volatilestorage unit 209. In some implementations the non-volatile storage unit209 includes a memory card or other form of nontransitory media, forstoring software and data. The storage unit 209 is optionally controlledby the controller 208.

In some implementations, the imaging device 100 optionally includes auser interface 200 including one or more input devices 204 (e.g., atouch screen, buttons, or switches) and/or an optional display 202.Additionally and/or alternatively, in some implementations, the imagingdevice 100 may be controlled by an external device such as a handhelddevice, a smartphone (or the like), a tablet computer, a laptopcomputer, a desktop computer, and/or a server system. To that end, theimaging device 100 includes one or more communication interfaces 152 forconnecting to any wired or wireless external device or communicationnetwork (e.g., a wide area network such as the Internet) 156. In someembodiments, imaging device 100 is very compact and docks directly ontoor with a handheld device, a smartphone (or the like), a tabletcomputer, and/or a laptop computer by an electronic interface. In someimplementations, imaging device 100 docks to a desktop computer (e.g.,via a docking station or USB connection. The imaging device 100 includesan internal bus 212 for interconnecting the aforementioned elements. Thecommunication bus 212 may include circuitry (sometimes called a chipset)that interconnects and controls communications between theaforementioned components.

In some implementations, the imaging device 100 communicates with acommunication network 156, thereby enabling the imaging device 100 totransmit and/or receive data between mobile communication devices overthe communication network, particularly one involving a wireless link,such as cellular, WiFi, ZigBee, BlueTooth, IEEE 802.11b, 802.11a,802.11g, or 802.11n, etc. The communication network can be any suitablecommunication network configured to support data transmissions. Suitablecommunication networks include, but are not limited to, cellularnetworks, wide area networks (WANs), local area networks (LANs), theInternet, IEEE 802.11b, 802.11a, 802.11g, or 802.11n wireless networks,landline, cable line, fiber-optic line, USB, etc. The imaging system,depending on an embodiment or desired functionality, can work completelyoffline by virtue of its own computing power, on a network by sendingraw or partially processed data, or both concurrently.

The system memory 220 includes high-speed random access memory, such asDRAM, SRAM, DDR RAM, or other random access solid state memory devices;and typically includes non-volatile memory flash memory devices, orother non-transitory solid state storage devices. The system memory 220optionally includes one or more storage devices remotely located fromthe CPU(s) 508. The system memory 220, or alternately the non-transitorymemory device(s) within system memory 220, comprises a non-transitorycomputer readable storage medium.

In some implementations, operation of the imaging device 100 iscontrolled primarily by an operating system 530, which is executed bythe CPU 210. The operating system 230 can be stored in the system memory220 and/or storage unit 209. In some embodiments, the image device 100is not controlled by an operating system, but rather by some othersuitable combination of hardware, firmware and software.

In some implementations, the system memory 220 includes one or more of afile system 232 for controlling access to the various files and datastructures described herein, an illumination software control module 234for controlling a light source associated and/or integrated with theimaging device 100, a photo-sensor control module 236, a sensor datastore 240 for storing hyperspectral image series A 242, including images243-1 to 243-N, acquired by photo-sensors (e.g. the photo-sensors 112),a data processing software module 250 for manipulating the acquiredsensor data, a hyperspectral data cube data store 260 for storinghyperspectral data cube A data 262, including data planes 263-1 to263-M, assembled from the acquired hyperspectral image series, and acommunication interface software control module 154 for controlling thecommunication interface 152 that connects to an external device (e.g., ahandheld device, laptop computer, or desktop computer) and/orcommunication network (e.g., a wide area network such as the Internet).

The acquired sensor data 242 and hyperspectral data cube data 262 can bestored in a storage module in the system memory 220, and do not need tobe concurrently present, depending on which stages of the analysis theimaging device 100 has performed at a given time. In someimplementations, prior to imaging a subject and after communicating theacquired sensor data or processed data files thereof, the imaging device100 contains neither acquired sensor data 242 nor the hyperspectral datacube data 262. In some implementations, after imaging a subject andafter communicating the acquired sensor data or processed data filesthereof, the imaging device 100 retains the acquired sensor data 242and/or hyperspectral data cube data 262 for a period of time (e.g.,until storage space is needed, for a predetermined amount of time,etc.).

In some implementations, the programs or software modules identifiedabove correspond to sets of instructions for performing a functiondescribed above. The sets of instructions can be executed by one or moreprocessors, e.g., a CPU(s) 210. The above identified software modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these programs or modules may be combined or otherwisere-arranged in various embodiments. In some embodiments, the systemmemory 220 stores a subset of the modules and data structures identifiedabove. Furthermore, the system memory 220 may store additional modulesand data structures not described above.

The system memory 220 optionally also includes one or more of thefollowing software modules, which are not illustrated in FIG. 2: aspectral library which includes profiles for a plurality of medicalconditions, a spectral analyzer software module to compare measuredspectral data to a spectral library, control modules for additionalsensors, information acquired by one or more additional sensors (such asa remote temperature measuring device), an image constructor softwaremodule for generating a spectral image, a spectral image assembled basedon a hyperspectral data cube and optionally fused with informationacquired by an additional sensor, a fusion software control module forintegrating data acquired by an additional sensor into a hyperspectraldata cube, and a display software control module for controlling abuilt-in display.

While examining a subject and/or viewing spectral images of the subject,a physician can optionally provide input to the image device 100 thatmodifies one or more parameters upon which a spectral image and/ordiagnostic output is based. In some implementations, this input isprovided using input device 204. Among other things, the image devicecan be controlled to modify the spectral portion selected by a spectralanalyzer (e.g., to modify a threshold of analytical sensitivity) or tomodify the appearance of the image generated by an image assembler(e.g., to switch from an intensity map to a topological rendering).

In some implementations, the imaging device 100 can be instructed tocommunicate instructions to an imaging subsystem to modify the sensingproperties of the photo-sensors 112 (e.g., an exposure setting, a framerate, an integration rate, or a wavelength to be detected). Otherparameters can also be modified. For example, the imaging device 100 canbe instructed to obtain a wide-view image of the subject for screeningpurposes, or to obtain a close-in image of a particular region ofinterest.

In some implementations, the imaging device 100 does not include acontroller 208 or storage unit 209. In some such implementations, thememory 220 and CPU 210 are one or more application-specific integratedcircuit chips (ASICs) and/or programmable logic devices (e.g. anFGPA—Field Programmable Gate Array). For example, in someimplementations, an ASIC and/or programmed FPGA includes theinstructions of the illumination control module 234, photo-sensorcontrol module 236, and/or the data processing module 250. In someimplementations, the ASIC and/or FPGA further includes storage space forthe acquired sensor data store 240 and the sensor data 242 storedtherein and/or the hyperspectral data cube data store 260 and thehyperspectral/multispectral data cubes 262 stored therein.

In some implementations, the system memory 220 includes a spectrallibrary and a spectral analyzer for comparing hyperspectral datagenerated by the image device 100 to known spectral patterns associatedwith various physiologic parameters and/or medical conditions. In someimplementations, analysis of the acquired hyperspectral data isperformed on an external device such as a handheld device, tabletcomputer, laptop computer, desktop computer, an external server, forexample in a cloud computing environment or processing and/or storagecenter 50.

In some implementations, a spectral library includes profiles for aplurality of physiologic arterial parameters and/or medical conditions,each of which contains a set of spectral characteristics unique to themedical condition. A spectral analyzer uses the spectral characteristicsto determine the probability that a region of the subject correspondingto a measured hyperspectral data cube is afflicted with a physiologicparameter and/or medical condition. In some implementations, eachprofile includes additional information about the physiologicalparameter and/or condition, e.g., information about whether thecondition is malignant or benign, options for treatment, etc. In someimplementations, each profile includes biological information, e.g.,information that is used to modify the detection conditions for subjectsof different skin types. In some implementations, the spectral libraryis stored in a single database. In other implementations, such data isinstead stored in a plurality of databases that may or may not all behosted by the same computer, e.g., on two or more computers addressableby wide area network. In some implementations, the spectral library iselectronically stored in the storage unit 220 and recalled using thecontroller 208 when needed during analysis of hyperspectral data cubedata.

In some implementations, the spectral analyzer analyzes a particularspectra derived from hyperspectral data cube data, the spectra havingpre-defined spectral ranges (e.g., spectral ranges specific for aparticular physiologic arterial parameter and/or medical condition), bycomparing the spectral characteristics of a pre-determined physiologicarterial parameter and/or medical condition to the subject's spectrawithin the defined spectral ranges. In some implementations, thepre-defined spectral ranges correspond to values of one or more ofdeoxyhemoglobin levels, oxyhemoglobin levels, total hemoglobin levels,oxygen saturation, oxygen perfusion, hydration levels, total hematocritlevels, melanin levels, and collagen levels of a tissue on a patient(e.g., an area 24 or 26 of the body of a subject 22). Performing such acomparison only within defined spectral ranges can both improve theaccuracy of the characterization and reduce the computational powerneeded to perform such a characterization.

In some implementations, the physiologic parameter is an arterialparameter selected from the group consisting of blood flow (e.g., bloodingress and/or egress), oxygen delivery, oxygen utilization, oxygensaturation, deoxyhemoglobin levels, oxyhemoglobin levels, totalhemoglobin levels, oxygen perfusion, hydration levels, and totalhematocrit levels.

In some implementations, the medical condition is selected from thegroup consisting of peripheral arterial disease (PAD), critical limbischemia, ulceration, gangrene, tissue ischemia, ulcer formation, ulcerprogression, pressure ulcer formation, pressure ulcer progression,diabetic foot ulcer formation, diabetic foot ulcer progression, venousstasis, venous ulcer disease, infection, shock, cardiac decompensation,respiratory insufficiency, hypovolemia, the progression of diabetes,congestive heart failure, sepsis, dehydration, hemorrhage, hypertension,detection of advanced glycemic end products (AGEs), exposure to achemical or biological agent, and an inflammatory response.

In some implementations, the spectral analyzer identifies a spectralsignature within the hyperspectral data cube that corresponds with aphysiologic parameter and/or medical condition of the patient. Incertain implementations, this is accomplished by identifying a patternof oxidation or hydration in a tissue associated with a tissue of thepatient. In some implementations, the analysis of the hyperspectral datacube includes performing at least one of adjusting the brightness of atleast one of the respective digital images in the hyperspectral datacube (e.g., data cube plane 362-M at wavelength range No. M), adjustingthe contrast of at least one of the respective digital images in thehyperspectral data cube, removing an artifact from at least one of therespective digital images in the hyperspectral data cube, processing oneor more sub-pixels of at least one of the respective digital images inthe hyperspectral data cube, and transforming a spectral hypercubeassembled from a plurality of digital images.

In some implementations, the display 202 receives an indication of aphysiologic parameter and/or medical condition (e.g., from an outputmodule), and displays the indication of the physiologic parameter and/ormedical condition. In some embodiments, an output module is a generaldisplay control module. In some implementations, the display 202receives an image (e.g., a color image, mono-wavelength image, orhyperspectral/multispectral image) from a display control module, anddisplays the image. Optionally, the display subsystem also displays alegend that contains additional information. For example, the legend candisplay information indicating the probability that a region has aparticular medical condition, a category of the condition, a probableage of the condition, the boundary of the condition, information abouttreatment of the condition, information indicating possible new areas ofinterest for examination, and/or information indicating possible newinformation that could be useful to obtain a diagnosis, e.g., anothertest or another spectral area that could be analyzed.

In some implementations, a housing display is built into the housing ofthe imaging device 100. In an example of such an implementation, a videodisplay in electronic communication with the processor 210 is included.In some implementations, the housing display is a touch screen displaythat is used to manipulate the displayed image and/or control the imagedevice 100.

In some implementations, the communication interface 152 comprises adocking station for a mobile device having a mobile device display. Amobile device, such as a smart phone, a personal digital assistant(PDA), an enterprise digital assistant, a tablet computer, an IPOD, adigital camera, a portable music player, or a wearable technology devicecan be connected to the docking station, effectively mounting the mobiledevice display onto the imaging device 100. Optionally, the mobiledevice is used to manipulate the displayed image and/or control theimage device 100.

In some implementations, the imaging device 100 is configured to be inwired or wireless communication with an external display, for example,on a handheld device, tablet computer, laptop computer, desktopcomputer, television, IPOD, projector unit, or wearable technologydevice, on which the image is displayed. Optionally, a user interface onthe external device is used to manipulate the displayed image and/orcontrol the imaging device 100.

In some implementations, an image can be displayed in real time on thedisplay. The real-time image can be used, for example, to focus an imageof the subject, to select an appropriate region of interest, and to zoomthe image of the subject in or out. In one embodiment, the real-timeimage of the subject is a color image captured by an optical detectorthat is not covered by a detector filter. In some implementations, theimager subsystem comprises an optical detector dedicated to capturingtrue color images of a subject. In some implementations, the real-timeimage of the subject is a mono-wavelength, or narrow-band (e.g., 10-50nm), image captured by an optical detector covered by a detector filter.In these embodiments, any optical detector covered by a detector filterin the imager subsystem may be used for: (i) resolving digital images ofthe subject for integration into a hyperspectral data cube, and (ii)resolving narrow-band images for focusing, or otherwise manipulating theoptical properties of the imaging device 100.

In some implementations, an indication of a physiologic parameter,medical condition, and/or hyperspectral image constructed from datacaptured by the photo-sensors 112 is displayed on an internal housingdisplay, mounted housing display, or external display. Assembledhyperspectral data (e.g., present in a hyperspectral/multispectral datacube) is used to create a two-dimensional representation of the imagedobject or subject, based on one or more parameters (e.g., a physiologicarterial parameter). An image constructor module, stored in the imagingsystem memory or in an external device, constructs an image based on,for example, one or more analyzed spectra. Specifically, the imageconstructor creates a representation of information within the one ormore spectra. In one example, the image constructor constructs atwo-dimensional intensity map in which the spatially-varying intensityof one or more particular wavelengths (or wavelength ranges) within theone or more spectra is represented by a corresponding spatially varyingintensity of a visible marker.

In some implementations, the image constructor fuses a hyperspectralimage with information obtained from one or more additional sensors.Non-limiting examples of suitable image fusion methods include: bandoverlay, high-pass filtering method, intensity hue-saturation, principlecomponent analysis, and discrete wavelet transform.

FIG. 3 is a schematic illustration of a hyperspectral data cube 262.

Hyperspectral sensors collect information as a set of images, which arereferred to herein as hyperspectral data cube planes 263. Each image 263represents a range of the electromagnetic spectrum and is also known asa spectral band. These images 263 are then combined and form athree-dimensional hyperspectral data cube 262 for processing andanalysis.

FIGS. 4A-4B are flow diagrams illustrating a method 400 of measuringtissue oxygenation. The method 400 is performed at an electronic devicehaving one or more processors and memory. In some implementations one ormore steps of the method are performed at an imaging system (e.g.,imaging system 100, FIG. 1; coaxial imaging system 500 employing a beamsteering element, FIG. 5; single-sensor imaging system 700 employingphoto-sensor and filter arrays, FIG. 7; or concurrent capture imagingsystem 800, FIG. 8).

The electronic device (e.g., a computer or imaging system) obtains (402)a data set (e.g., hyperspectral image series 242 or hyperspectral datacube 262) including a plurality of images (e.g., images 231) of a tissueof interest. Each respective image in the plurality of images isresolved at a different spectral band, in a predetermined set of eightto twelve spectral bands, and includes an array of pixel values. For ininstance, in some embodiments, each respective image comprises 500,000or more pixel values, 1,000,000 or more pixel values, 1,100,000 or morepixel values, 1,200,000 or more pixel values, or 1,300,000 or moremeasured pixel values. In some implementations, the hyperspectral dataset also includes data from images resolved at spectral bands other thanthose of the predetermined set of eight to ten spectral bands (e.g.,data that will not be included in the processing steps describedherein).

In some implementations, the method includes capturing (404) theplurality of images of the tissue of interest at an imaging system(e.g., imaging system 100, FIG. 1; coaxial imaging system 500 employinga beam steering element, FIG. 5; single-sensor imaging system 700employing photo-sensor and filter arrays, FIG. 7; or concurrent captureimaging system 800, FIG. 8).

In some implementations, the imaging system captures (406) all of theplurality of images concurrently (e.g., when using a single-sensorimaging system 700 employing photo-sensor and filter arrays, FIG. 7; orconcurrent capture imaging system 800 employing eight or more imagesensors, FIG. 8).

In some implementations, hyperspectral imaging system captures (408) afirst subset of the plurality of images concurrently at a first timepoint, and captures a second subset of the plurality of images at asecond time point other than the first time point. For example, aconcurrent capture imaging system (e.g., system 800 in FIG. 8)concurrently captures four images, one each at photo-sensors 112-1 to112-4, each image at a different spectral band in the predetermined setof eight to ten spectral bands, in a first capture event. The concurrentcapture imaging system then concurrently captures four more images, oneeach at photo-sensors 112-1 to 112-4, each image at a different spectralband in the predetermined set of eight to ten spectral bands, in asecond capture event. As such, the concurrent capture imaging systemcaptures images at eight of the predetermined set of eight to tenspectral bands between the first and second capture events. In someimplementations, more than three capture events (e.g., three, four, orfive capture events) can be used to capture images at all thepredetermined set of eight to twelve spectral bands.

In some implementations, collecting the hyperspectral image includesilluminating the tissue of the subject with a first light (e.g., withlight source 120 in FIGS. 1B, 5, 6, and 8), the first light including afirst subset of spectral bands in the predetermined set of spectralbands. In some implementations, the light used to illuminate the regionof interest is polarized to improve the signal-to-noise ratio ofbackscattered light detected by the imaging system. Use of a polarizingfilter, orthogonal to a polarization of an illuminating light, in frontof the detector reduces non-polarized ambient light from the detectedsignal.

In some implementations, capturing the hyperspectral image includesconcurrently capturing a first subset of images in the plurality ofimages while illuminated with light corresponding to the spectral bandsbeing captured, each respective image in the first plurality of imagescaptured at a unique spectral band in the first plurality of spectralbands. In other words, images are captured at multiple spectral bandswhile the region of interest is illuminated with matching light.

In some implementations, each respective image in the first subset ofimages (e.g., each image 234 in hyperspectral image series A 242 in FIG.2) is captured with a unique optical detector in a plurality of opticaldetectors (e.g., with a respective optical detector 112 in a concurrentcapture imaging system 800 as illustrated in FIG. 8). For example, insome embodiments, each optical detector 112 is covered with a respectivefilter 114, allowing light corresponding to a unique spectral band inthe first plurality of spectral bands to pass to the detector 112. Inthis fashion, the images concurrently collected by each of the opticaldetectors 112 are combined to form a portion of, or the entirety of,image series A 242.

In some implementations, e.g., when images of the subject are capturedat less than all of the wavelengths in the predetermined set of spectralbands when illuminated with the first light, the method further includesilluminating the tissue with a second light (e.g., with light source 120in FIGS. 1B, 5, 6, and 8), the second light including a second subset ofspectral bands in the predetermined set of spectral bands, e.g., wherethe second subset of spectral bands is other than the first subset ofspectral bands.

In some implementations, the first light and the second light areirradiated from separate light sources. In some implementations, thelight used to illuminate the region of interest is polarized to improvethe signal-to-noise ratio of backscattered light detected by the imagingsystem. Use of a polarizing filter, orthogonal to the polarization ofthe illuminating light, in front of the detector reduces non-polarizedambient light and light reflected directly off the surface being imagesfrom the detected signal.

In some implementations, collecting the hyperspectral image includesconcurrently collecting a second subset of images in the plurality ofimages of the region of interest of the subject (e.g., images 243 inimage series A 242 in FIG. 2) while illuminated by the second light,each respective image in the second subset of images collected at aunique spectral band in the second subset of spectral bands. In otherwords, a second set of images is collected at multiple spectral bandswhile the region of interest is illuminated with matching light. Thesecond set of images complements the first set of images, such that allimages required for a hyperspectral image series (e.g., series A 242 inFIG. 2) are collected between the first and second set of images.

In some implementations, each respective image in the first subset ofimages is collected with a unique optical detector in a plurality ofoptical detectors, each respective image in the second subset of imagesis collected with unique optical detector in the plurality of opticaldetectors, and at least one optical detector in the plurality of opticaldetectors collects a respective image in the first subset of images anda respective image in the second subset of images. In other words, insome implementations, an imaging system having more than one imagingsensor (e.g., a concurrent capture imaging system 800, as illustrated inFIG. 8) is used, and at least one of the optical detectors (e.g.,optical detector 112-1 in FIG. 8) is used to collect a first image(e.g., in the first subset of images) at a first spectral band and thena second image (e.g., in the second subset of images) at a secondspectral band.

In some embodiments, the optical detector (e.g., optical detector 112-1in FIG. 8) is covered by a dual bandpass filter (e.g., filter 114-1 inFIG. 8) that allows light of the first spectral band and light of thesecond spectral band to pass through to the optical detector. In thisfashion, the region of interest of the subject is first illuminated withlight that includes the first spectral band, but not the second spectralband, and the first image is captured by the optical detector (e.g.,optical detector 112-1 in FIG. 8). Then, the region of interest of thesubject is illuminated with light that includes the second spectralband, but not the first spectral band, and the second image is capturesby the optical detector (e.g., the same optical detector 112-1 in FIG.8). Thus, the optical detector (e.g., optical detector 112-1 in FIG. 8)is used to collect two images, at different spectral bands, of thehyperspectral image series (e.g., image 243-B and image 243-C in imageseries A 242, represented in FIG. 2).

In some implementations, each respective optical detector in theplurality of optical detectors (e.g., each of optical detectors 112-1 to112-4, illustrated in FIG. 8) collects (428) a respective image in thefirst subset of images and a respective image in the second subset ofimages. In some implementations, each optical detector (e.g., opticaldetectors 112 in FIG. 8) is covered by a unique dual band pass filter(e.g., filters 114 in FIG. 8). In this fashion, the region of interestof the subject is illuminated with a first light having spectral bandscorresponding to one of the band passes on each of the filters, but notlight having spectral bands corresponding to the other band passes oneach of the filters (e.g., light emitted from first light source 120-1).A first sub-set of images is collected while the location is illuminatedwith the first light. Then, the location is illuminated with a secondlight having spectral bands corresponding to the other spectral bandpass on each of the filters, but not light having wavelengthscorresponding to the first band pass on each of the filters (e.g., lightemitted from second light source 120-2). A second sub-set of images isthen collected while the location is illuminated with the second light.

In some implementations, the first subset of images is four images andthe second subset of images is four images. For example, in someimplementations, an imaging system having four optical detectors (e.g.,concurrent capture imaging system 800 in FIG. 8) is used. Each opticaldetector (e.g., optical detectors 112) collects an image in the firstsubset and an image in the second subset of images, to form ahyperspectral image series consisting of eight images.

In some implementations, each respective optical detector in theplurality of optical detectors (e.g., optical detectors 112 of ahyperspectral imaging system such as the concurrent capture imagingsystem 800 illustrated in FIG. 8) is covered by a dual-band pass filter(e.g., filters 114 in FIG. 800).

In some implementations, each respective optical detector is covered bya triple bandpass filter, enabling use of a third light source andcollection of three sets of images at unique spectral bands. Forexample, four optical detectors can collect images at up to twelveunique spectral bands, when each detector is covered by a triplebandpass filter.

In some implementations, each respective optical detector is covered bya quad-bandpass filter, enabling use of a fourth light source andcollection of four sets of images at unique spectral bands. For example,four optical detectors can collect images at up to sixteen uniquespectral bands, when each detector is covered by a quad band-passfilter. In yet other implementations, bandpass filters allowing passageof five, six, seven, or more bands each can be used to collect largersets of unique spectral bands.

The method further includes, registering (411), using the processor, theplurality of images on a pixel-by-pixel basis, to form a plurality ofregistered images of the tissue. In some implementations, registeringincludes storing each respective image at a corresponding memorylocation (e.g., in memory 220), and comparing, on a pixel-by-pixel basis(e.g., with processor 210) each pixel of the respective images toproduce the plurality of registered images. In some implementations, oneor more registered images is then stored at a corresponding memorylocation.

In some implementations (e.g., where the methods includes capturingimages at an imaging system), the method includes performing spectralanalysis at the imaging system 100 (e.g., the system captures and thenprocesses the image). In other implementations, the imaging system 100captures the images, and then transmits the images, or pre-processeddata (e.g., a hypercube), to an external processing device (e.g., localprocessing device 24 or remote processing server 52) for spectralanalysis.

The electronic device then performs (412) spectral analysis at aplurality of points in a two-dimensional area of the plurality ofregistered images of the tissue (e.g., evaluates absorbance at the samepoints or groups of points in each of the images captured at thepredetermined set of spectral bands), the spectral analysis includingdetermining approximate values of oxyhemoglobin levels anddeoxyhemoglobin levels at each respective point in the plurality ofpoints.

In some implementations, the device performs spectral analysis byresolving (414) absorption signals at each point in the plurality ofpoints, accounting for a melanin contribution and loss of signal fromdiffuse scattering at each point in the plurality of points, to form aplurality of corrected absorption signals, and determining approximatevalues of oxyhemoglobin levels and deoxyhemoglobin levels from thecorrected absorption signals at each point in the plurality of points.

Algorithms for determining oxyhemoglobin and deoxyhemoglobin fromhyperspectral data are known in the art. For example, exemplaryprocessing algorithms are described in U.S. Pat. No. 8,644,911, thedisclosure of which is hereby expressly incorporated by reference, inits entirety, for all purposes. Advantageously, the present disclosurereduces the computational burden of determining oxyhemoglobin levels anddeoxyhemoglobin, when using algorithms disclosed in the art, byfacilitating accurate determination with significantly few wavelengths(e.g., using eight to ten wavelengths rather than fifteen or more).

In some implementations, the electronic device (e.g., imaging device100, local processing device 24, or remote processing server 52) models(416) the contribution provided by melanin and the losses provided bydiffuse scattering (e.g., background contributions) to the plurality oftissue oxygenation measurements collectively as a second orderpolynomial. For example, U.S. Pat. No. 8,644,911 describes an exemplarymethod for modeling contributions from melanin and losses provided bydiffuse scattering as a second order polynomial. In otherimplementations, background contributions (e.g., melanin absorption andloss due to diffuse scattering) may be modeled according to any linearor non-linear model known in the art.

In some implementations, the predetermined set of spectral bandsincludes eight spectral bands having central wavelengths of about 510nm, 530 nm, 540 nm, 560 nm, 580 nm, 590 nm, 620 nm, and 660 nm. In someimplementations the predetermined set is a set of twelve spectral bands,including these eight. In some implementations the predetermined set isa set of eleven spectral bands, including these eight. In someimplementations the predetermined set is a set of ten spectral bands,including these eight. In some implementations the predetermined set isa set of nine spectral bands, including these eight. In someimplementations, the predetermined set only includes these eightspectral bands.

In a specific implementation, the predetermined set of spectral bandsincludes eight spectral bands having central wavelengths of 510±2 nm,530±2 nm, 540±2 nm, 560±2 nm, 580±2 nm, 590±2 nm, 620±2 nm, and 660±2nm, and each spectral band in the eight spectral bands has a full widthat half maximum of less than 10 nm (408).

In some implementations, the predetermined set of spectral bandsincludes eight spectral bands having central wavelengths of about 520nm, 540 nm, 560 nm, 580 nm, 590 nm, 610 nm, 620 nm, and 640 nm. In someimplementations the predetermined set is a set of twelve spectral bands,including these eight. In some implementations the predetermined set isa set of eleven spectral bands, including these eight. In someimplementations the predetermined set is a set of ten spectral bands,including these eight. In some implementations the predetermined set isa set of nine spectral bands, including these eight. In someimplementations, the predetermined set only includes these eightspectral bands.

In another specific implementation, the predetermined set of spectralbands includes eight spectral bands having central wavelengths of 520±2nm, 540±2 nm, 560±2 nm, 580±2 nm, 590±2 nm, 610±2 nm, 620±2 nm, and640±2, and each spectral band in the eight spectral bands has a fullwidth at half maximum of less than 10 nm (409).

In some implementations, the predetermined set of spectral bandsconsists of eight spectral bands having central wavelengths of about 500nm, 530 nm, 545 nm, 570 nm, 585 nm, 600 nm, 615 nm, and 640 nm. In someimplementations the predetermined set is a set of twelve spectral bands,including these eight. In some implementations the predetermined set isa set of eleven spectral bands, including these eight. In someimplementations the predetermined set is a set of ten spectral bands,including these eight. In some implementations the predetermined set isa set of nine spectral bands, including these eight. In someimplementations, the predetermined set only includes these eightspectral bands.

In another specific implementation, the predetermined set of spectralbands includes eight spectral bands having central wavelengths of 500±2nm, 530±2 nm, 545±2 nm, 570±2 nm, 585±2 nm, 600±2 nm, 615±2 nm, and640±2 nm, and each spectral band in the eight spectral bands has a fullwidth at half maximum of less than 10 nm (410).

Use of the term “about,” for purposes of this particular set of spectralbands, refers to a central wavelength that is no more than 5 nm from therecited wavelength. In some implementations, each spectral band in theset has a central wavelength that is no more than 4 nm from the recitedwavelength. In some implementations, each spectral band in the set has acentral wavelength that is no more than 3 nm from the recitedwavelength. In some implementations, each spectral band in the set has acentral wavelength that is no more than 2 nm from the recitedwavelength. In some implementations, each spectral band in the set has acentral wavelength that is no more than 1 nm from the recitedwavelength. In some implementations, each spectral band in the set hasthe recited central wavelength.

In some implementations, each respective spectral band has a full widthat half maximum of less than 20 nm. In some implementations, eachrespective spectral band has a full width at half maximum of less than15 nm (422). In some implementations, each respective spectral band hasa full width at half maximum of less than 10 nm. In someimplementations, each respective spectral band has a full width at halfmaximum of less than 5 nm (424). In some implementations, eachrespective spectral band has a full width at half maximum of less than 4nm. In some implementations, each respective spectral band has a fullwidth at half maximum of less than 3 nm. In some implementations, eachrespective spectral band has a full width at half maximum of less than 2nm. In some implementations, each respective spectral band has a fullwidth at half maximum of no more than 1 nm.

In some implementations, the imaging system is handheld and batteryoperated. This is accomplished by reducing the power budget needed tooperate the hyperspectral imaging system. In non-limiting examples, thepower budget is reduced by one or more of: using orthogonal polarizingfilters in front of the illumination source (e.g., light source 120 inFIG. 1B; illumination subsystem 510 in FIG. 5; or illumination source120 in FIG. 8) and detection source (e.g., sensor module 110 in FIG. 1B,optical detectors 112 in FIG. 5 and FIG. 8); using matched narrowbandirradiation sources (e.g., LED light sources emitting one or more narrowspectral bands) and detection filters (e.g., notch or other narrow bandfilters); using capacitors to store large current bursts needed forefficient illumination of the target (e.g., a tissue); and reducing thenumber of spectral bands required to construct a high resolutionhyperspectral image (e.g., using only eight to ten spectral bands).

In some embodiments, the method further includes providing a therapy fora medical condition based on the tissue oxygenation measurements. Insome implementations, the medical condition is peripheral arterialdisease (PAD), critical limb ischemia, ulceration, gangrene, tissueischemia, ulcer formation, ulcer progression, pressure ulcer formation,pressure ulcer progression, diabetic foot ulcer formation, diabetic footulcer progression, venous stasis, venous ulcer disease, infection,shock, cardiac decompensation, respiratory insufficiency, hypovolemia,the progression of diabetes, congestive heart failure, sepsis,dehydration, hemorrhage, hypertension, exposure to a chemical orbiological agent, an inflammatory response, or a cancer.

In some implementations, the method further includes providing adiagnosis of a medical condition based on the tissue oxygenationmeasurements. In some implementations, the medical condition isperipheral arterial disease (PAD), critical limb ischemia, ulceration,gangrene, tissue ischemia, ulcer formation, ulcer progression, pressureulcer formation, pressure ulcer progression, diabetic foot ulcerformation, diabetic foot ulcer progression, venous stasis, venous ulcerdisease, infection, shock, cardiac decompensation, respiratoryinsufficiency, hypovolemia, the progression of diabetes, congestiveheart failure, sepsis, dehydration, hemorrhage, hypertension, exposureto a chemical or biological agent, or an inflammatory response.

In some implementations, the method further includes providing aprognosis for progression, regression, recurrence, or disease-freesurvival of a medical condition based on the tissue oxygenationmeasurements. In some implementations, the medical condition isperipheral arterial disease (PAD), critical limb ischemia, ulceration,gangrene, tissue ischemia, ulcer formation, ulcer progression, pressureulcer formation, pressure ulcer progression, diabetic foot ulcerformation, diabetic foot ulcer progression, venous stasis, venous ulcerdisease, infection, shock, cardiac decompensation, respiratoryinsufficiency, hypovolemia, the progression of diabetes, congestiveheart failure, sepsis, dehydration, hemorrhage, hypertension, exposureto a chemical or biological agent, or an inflammatory response.

In some implementations, the method further includes assigning a therapyfor a medical condition based on the tissue oxygenation measurements. Insome implementations, the medical condition is peripheral arterialdisease (PAD), critical limb ischemia, ulceration, gangrene, tissueischemia, ulcer formation, ulcer progression, pressure ulcer formation,pressure ulcer progression, diabetic foot ulcer formation, diabetic footulcer progression, venous stasis, venous ulcer disease, infection,shock, cardiac decompensation, respiratory insufficiency, hypovolemia,the progression of diabetes, congestive heart failure, sepsis,dehydration, hemorrhage, hypertension, exposure to a chemical orbiological agent, or an inflammatory response.

In some embodiments, the method further includes providing apreventative therapy for a medical condition based on the tissueoxygenation measurements. For example, hyperspectral analysis ofdiabetic patients may identify hot spots indicating emerging foot ulcersthat have not yet been ulcerated. In some implementations, the medicalcondition is peripheral arterial disease (PAD), critical limb ischemia,ulceration, gangrene, tissue ischemia, ulcer formation, ulcerprogression, pressure ulcer formation, pressure ulcer progression,diabetic foot ulcer formation, diabetic foot ulcer progression, venousstasis, venous ulcer disease, infection, shock, cardiac decompensation,respiratory insufficiency, hypovolemia, the progression of diabetes,congestive heart failure, sepsis, dehydration, hemorrhage, hypertension,exposure to a chemical or biological agent, or an inflammatory response.

Exemplary Implementations

In some implementations, the methods described herein are performedusing imaging systems with unique internal optical architectures thatallow for faster image acquisition and data processing. FIGS. 5 and 6illustrate one such implementation in which the imaging system has abeam steering element configured to steer light to one of a plurality ofoptical detectors, each of which are configured to resolve light of aspecific spectral band. FIG. 7 illustrates the principle behind a secondsuch implementation, in which the imaging system employs a photo-sensorarray having a plurality of photo-sensors, covered by a spectral filterarray having a plurality of filter elements. This implementation enablescapture of images at all wavelengths necessary to construct ahyperspectral image with a single exposure. FIG. 8 illustrates theprinciple behind a third such implementation, in which the imagingsystem concurrently captures multiple images at multiple spectral bandsby splitting the incidental light and directing it to multiple opticaldetectors.

FIG. 5 illustrates the use of an imaging system including a beamsteering element having a plurality of operating modes, which directslight of different wavelengths to distinct optical detectors from acommon point of origin, thus maintaining co-axial alignment betweenimages captured by the respective optical detectors. In oneimplementation, the imaging device includes a housing having an exteriorand an interior and at least one objective lens attached to or withinthe housing. The at least one objective lens is disposed in an opticalcommunication path comprising an originating end and a terminating end.The imaging device also includes a beam steering element within theinterior of the housing. The beam steering element is in opticalcommunication with the at least one objective lens and is positioned atthe terminating end of the optical communication path. The beam steeringelement is characterized by a plurality of operating modes. Eachrespective operating mode in the plurality of operating modes causes thebeam steering element to be in optical communication with a differentoptical detector.

According to certain embodiments, the co-axial imaging device 500includes: an illumination subsystem 510 containing one or more lightsources 120; an objective lens assembly 520 housed in a chassis 522 thatanchors the lens assembly with respect to other components of theoptical assembly; an optional stray light shield 524; a beam steeringelement 530 in electrical communication, and optionally mounted on, amotherboard 540 in electrical communication with one or more CPU(s) (notshown); and an imager subsystem comprising a plurality of opticaldetectors 112 in electrical communication with the motherboard 540 byway of a flex circuit or wire 542.

In one embodiment, an optical communication path is created whenradiation emitted from one or more of the lights 120 of the illuminationsubsystem 510 illuminates a tissue of the subject (not shown) and isbackscattered to an objective lens assembly 520, which focuses the lighton a beam steering element 530 having a plurality of operating modes.When positioned in a respective operating mode, the beam steeringelement 530 reflects the light onto one of the plurality of opticaldetectors 112, which is configured to capture an image of the surface ofthe subject at one or more specific wavelengths.

Each optical detector 112 in the imager subsystem is optionally coveredby an optical filter (e.g., a detector filter), which allows light of apredetermined wavelength to pass through to the detector. In oneembodiment, one or more of the light sources 120 is matched to a filtercovering an optical detector 112, e.g., the light emits radiation atwavelength that is capable of passing through the corresponding filter.When respective light sources 120 in a plurality of light sources arematched to corresponding detector filters in a plurality of detectorfilters, the beam steering element 530 functions to direct radiationemitted by a respective light source 120 to the corresponding opticaldetector 112 covered by a matching filter. The beam steering element 530is configured to have a plurality of operating modes, each of whichdirects light backscattered from the tissue of the subject to adifferent optical detector 112.

The internal hardware of co-axial imaging device 500 is mounted inhousing 552, according to some embodiments. Optionally, housing 552includes dock 560 for attaching portable device 562 to housing 552.Optionally, portable device 562 contains a display, preferably atouch-screen display, for displaying images acquired by internalhardware of a co-axial imaging device 500.

Referring to FIG. 6, light 28 having a first wavelength (λ), emittedfrom a light source 120, reflects or backscatters from a region ofinterest (24; ROI) on an object or subject 22. The light 28 then passesthrough the objective lens assembly (not shown) and is directed by abeam steering element 530, positioned in a first operating mode in aplurality of operating modes, towards an optical detector 112 configuredto resolve light of the first wavelength (λ). In certain embodiments,the beam steering element is positioned in its respective operatingmodes through the use of an actuator 610 capable of adjust tip and tiltangles of the beam steering element.

In some embodiments, control modules, stored in the system memory 220control: the illumination, via an illumination control module 234, thedirection of the beam towards one or more optical detectors 112 via abeam steering control module 620, and the image exposure time andoptical detectors themselves via an optical detector control module 236.The beam steering control module 620 directs actuator 610 to place thebeam steering element 530 in various operating modes, each of which isin optical communication with one of the optical detectors 112.

For example, to collect images of an object 22 forhyperspectral/multispectral analysis at two different wavelengths, λ andλ′, the illumination control module 234 turns on a first light 120-1,emitting light 28-1 at a first wavelength (λ), illuminating a region ofinterest (ROI) 24 on the subject 22. Reflected or backscattered light120-1 from the subject 22 enters the objective lens or assembly thereof(not shown) and hits the beam steering element 530, placed in a firstoperating mode by an actuator 610 controlled by the beam steeringcontrol module 620, which redirects the light onto an optical detector112-1 configured to resolve light of wavelength λ. The illuminationcontrol module 234 then turns off the first light 120-1 and turns on asecond light 120-2, emitting light 28-2 at a second wavelength (λ′),illuminating the ROI 24. Concurrently, the beam steering control module620 instructs the actuator 610 to place the beam steering element 530 ina second operating mode, which is in optical communication with a secondoptical detector 112-2 configured to resolve light of wavelength λ′.Thus, when reflected or backscattered light 28-2 hits the beam steeringelement 530, the light 28-2 is redirected onto the second opticaldetector 112-2.

The beam steering element 530 can be one or more reflective elementscapable of redirecting the incident beam in one or more directionstoward the detector(s). In some embodiments, the beam steering element530 is an element that reflects light in one or more directions (e.g., amirror element). In a particular embodiment the beam steering element isa plain mirror capable of reflecting light over a wide range ofwavelengths. In another particular embodiment, the beam steering elementis an array of mirrors, for example an array of micromirrors.

In one embodiment, the beam steering element consists of more than oneelement and is capable of concurrently directing lights of differentwavelengths in different directions. In specific embodiments, the beamsteering element includes a first hot mirror and a second mirrorpositioned behind the hot mirror. The hot mirror is suitably coated toreflect light above or below a certain wavelength, while beingtransparent to light with lower or higher wavelengths, respectively.

Further implementations of the co-axial hyperspectral imaging strategyare disclosed in International Publication No. WO 2014/007869, thecontent of which is expressly incorporated herein by reference, in itsentirety, for all purposes.

In some implementations, the method is performed using an imaging deviceincluding a photo-sensor array including a plurality of photo-sensors.Each photo-sensor provides a respective output. The device furthercomprises a spectral filter array having a plurality of filter elements.Each filter element is arranged to filter light received by a respectiveone or more of the photo-sensors. Each filter element is one of aplurality of filter-types. Each filter-type characterized by a uniquespectral pass-band. The device further includes an interface module toselect a plurality of subsets of photo-sensor outputs. Each such subsetis associated with a single respective filter-type. The device comprisesa control module that generates a hyperspectral data cube from thesubsets of photo-sensor outputs by generating a plurality of images.Each such image is produced from a single corresponding subset ofphoto-sensor outputs in the plurality of photo-sensor outputs and so isassociated with a corresponding filter-type in the plurality offilter-types.

FIG. 7 is an exploded schematic view of an implementation of an imagesensor assembly for a single-sensor imaging device 700. The image sensorassembly includes a photo-sensory array 112 in combination with a filterarray 114. In some implementations, the photo-sensory array 112 includesa plurality of photo-sensors. For example, detailed view 710schematically shows, as a non-limiting example only, a number ofphoto-sensors 711 included in the photo-sensor array 112. Eachphoto-sensor 711 generates a respective electrical output by convertinglight incident on the photo-sensor.

The light incident on a particular photo-sensor 711 is filtered by arespective filter in the filter array 114. In some implementations, thefilter array 114 is configured to include a plurality of filterelements. Each filter element is arranged to filter light received by arespective one or more of the plurality of photo-sensors in thephoto-sensor array 112. Each filter element is also one of a pluralityof filter-types, and each filter-type is characterized by a spectralpass-band different from the other filter-types. As such, the electricaloutput of a particular photo-sensor is associated with a particularspectral pass-band associated with the respective filter associated theparticular photo-sensor 711.

For example, the detailed view 720 schematically shows, as anon-limiting example only, a number of filter-types A, B, C, D, E, F, G,H, and I are included in the filter array 114. In one implementation, atleast two of filter types A, B, C, D, E, F, G, H, and I have differentspectral pass-bands. For example, as illustrated in FIG. 7, filterelements 721 a-1 and 721 a-2 of filter types A and B, respectively, havedifferent spectral pass-bands. In some implementations, at least two offilter types A, B, C, D, E, F, G, H, and I have the same spectralpass-band and at least two of filter types A, B, C, D, E, F, G, H, and Ihave different spectral pass-bands.

In some implementations, each filter-type A, B, C, D, E, F, G, H, and Ihas a spectral pass-band different from the others. In someimplementations, the filter-types A, B, C, D, E, F, G, H, and I arearranged in a 3×3 grid that is repeated across the filter array 114. Forexample, as illustrated in FIG. 7, three filter elements 721 a-1, 721b-1, 721 c-1 of filter-type A are illustrated to show that instances offilter-type A are repeated in a uniform distribution across the filterarray 114 such that the center-to-center distance dl between two filtersof the same type is less than 250 microns in some implementations. Insome implementations, the center-to-center distance dl between twofilters of the same type is less than 100 microns.

Moreover, while nine filter-types are illustrated for example in FIG. 7,those skilled in the art will appreciate from the present disclosurethat any number of filter types can be used in various implementations.For example, in some implementations 3, 5, 16 or 25 filter-types can beused in various implementations. Additionally and/or alternatively,while a uniform distribution of filter-types has been illustrated anddescribed, those skilled in the art will appreciate from the presentdisclosure that, in various implementations, one or more filter-typesmay be distributed across a filter array in a non-uniform distribution.Additionally and/or alternatively, those skilled in the art will alsoappreciate that “white-light” or transparent filter elements may beincluded as one of the filter-types in a filter array.

FIG. 7 illustrates an advantage of the single-sensor imaging device. Asingle exposure of light 30 from a lens assembly is filtered by filterarray 114 to form filtered light 32 that impinges upon sensor 112 and,from this single exposure, multiple images 243 of the same region 24 ofa subject 22 are concurrently made. The imaging device 700 includes aphoto-sensor array 112 including a plurality of photo-sensors 711. Eachphoto-sensor 711 provides a respective output. Imaging device 700further includes a spectral filter array 114 having a plurality offilter elements 721. Each filter element 721 is arranged to filter light30 received by a respective one or more of the plurality ofphoto-sensors 711. Each filter element 721 is one of a plurality offilter-types. For instance, in FIG. 7, each filter element 721 is one offilter types A, B, C, D, E, F, G, H, and I, with each respectivefilter-type characterized by a spectral pass-band different from theother filter-types.

An interface module selects one or more subsets of photo-sensor 711outputs. Each subset of photo-sensor 711 outputs is associated with(receives light exclusively through) a single respective filter-type.For instance, in one such subset are the photo-sensors 711 that areassociated with (receive light exclusively from) filter type A, anothersuch subset are the photo-sensors 711 that are associated with filtertype B and so forth. A control module is configured to generate ahyperspectral data cube 262 from the one or more sub-sets ofphoto-sensor outputs by generating a plurality of respective images 263.In some embodiments, each respective image 263 in the plurality ofimages is produced from a single respective sub-set of photo-sensoroutputs 711 so that each respective image 263 in the plurality of imagesis associated with a particular filter-type. Thus, for example,referring to FIG. 7, all the photo-sensors 711 that receive filteredlight from filter elements 721 of filter type A are used to form a firstimage 263-1, all the photo-sensors 711 that receive filtered light fromfilter elements 721 of filter type B are used to form a second image263-2, all the photo-sensors 711 that receive filtered light from filterelements 721 of filter type C are used to form a third image 263-3, andso forth thereby creating a hyperspectral data cube 262 from the one ormore sub-sets of photo-sensor outputs. The hyperspectral data cube 262comprises the plurality of images, each image being of the same regionof a subject but at a different wavelength or wavelength ranges.

The concept disclosed in FIG. 7 is highly advantageous because multiplelight exposures do not need to be used to acquire all the images 263needed to form the hyperspectral data cube 262. In some embodiments, asingle light exposure is used to concurrently acquire each image 263.This is made possible because the spatial resolution of the sensor 112exceeds the resolution necessary for an image 263. Thus, rather thanusing all the pixels in the sensor 112 to form each image 263, thepixels can be divided up in the manner illustrated in FIG. 7, forexample, using filter plate 114 so that all the images are takenconcurrently. In some implementations, the spectral pass-bands of thefilter-elements used in a filter array 114 correspond to a predeterminedset of spectral bands, e.g., used to identify a particular type ofspectral signature in an object (e.g., in a tissue of a subject).

In one implementation, an imaging device comprises a filter array 114containing a first set of filter elements sufficient to distinguishspectral signatures related to a first medical condition (e.g., apressure ulcer) from healthy tissue (e.g., non-ulcerated tissue). In oneimplementation, the filter array 114 of the imaging device furthercontains a second set of filter elements sufficient to distinguishspectral signatures related to a second medical condition (e.g., acancerous tissue) from healthy tissue (e.g., a non-cancerous tissue). Insome implementations, the first set of filter elements and the secondset of filter elements may overlap, such that a particular filterelement is used for investigation of both types of medical conditions.Accordingly, in some implementations, the imaging device will have aplurality of imaging modalities, each individual imaging modalityrelated to the investigation of a different medical condition.

Further implementations of the single-sensor imaging device aredisclosed in International Publication No. WO 2014/063117, the contentof which is expressly incorporated herein by reference, in its entirety,for all purposes.

In some implementations, a similar effect can be achieved by placingmultiple imager chips in an array (e.g., a 2×2, 3×3, 4×4, or 5×5 array).To minimize off axis imaging errors, individual imager dies may bearranged in a tight, multi-chip module configuration.

In some implementations, the method is performed using an imaging devicethat concurrently captures multiple images, where each image representsa desired spectral band. Specifically, the imaging device uses multiplephoto-sensors and beam splitting elements to capture a plurality ofimages concurrently. Thus, a user does not need to maintain perfectalignment between the imaging device and a subject while attempting tocapture multiple discrete images, and can instead simply align theimaging device once and capture all of the required images in a singleoperation of the imaging device.

FIG. 8 is an exploded schematic view of an optical assembly of anexemplary concurrent capture imaging system, in accordance with someimplementations, in which the optical paths formed by the optical pathassembly are shown. In some implementations, the imager includes asingle light source 120. In other implementations, as shown in FIG. 8,the imager contains two or more light sources 120, configured to emitlight having different spectral bands (e.g., partially overlapping ornon-overlapping). In some implementations, the light sources emit thesame spectral bands, but are differentially filtered (e.g., by a filterplaced in front of the light sources) such that the illuminating lightfrom each light source has different spectral bands (e.g., partiallyoverlapping or non-overlapping). The optical path assembly channelslight received by the lens assembly 520 (e.g., illuminating lightemitted from light source 120 and backscattered from the region ofinterest on the patient) to the various photo-sensors 112 of the opticalassembly.

Turning to FIG. 8, the optical assembly includes a first beam splitter810-1, a second beam splitter 810-2, and a third beam splitter 810-3.Each beam splitter is configured to split the light received by the beamsplitter into at least two optical paths. For example, beam splittersfor use in the optical path assembly may split an incoming beam into oneoutput beam that is collinear to the input beam, and another output beamthat is perpendicular to the input beam.

Specifically, the first beam splitter 810-1 is in direct opticalcommunication with the lens assembly 52, and splits the incoming light(represented by arrow 30) into a first optical path and a second opticalpath. The first optical path is substantially collinear with the lightentering the first beam splitter 810-1, and passes to the second beamsplitter 810-2. The second optical path is substantially perpendicularto the light entering the first beam splitter 810-1, and passes to thethird beam splitter 810-3. In some implementations, the first beamsplitter 810-1 is a 50:50 beam splitter. In other implementations, thefirst beam splitter 810-1 is a dichroic beam splitter.

The second beam splitter 810-2 is adjacent to the first beam splitter810-1 (and is in direct optical communication with the first beamsplitter 810-1), and splits the incoming light from the first beamsplitter 810-1 into a third optical path and a fourth optical path. Thethird optical path is substantially collinear with the light enteringthe second beam splitter 810-2, and passes through to the first beamsteering element 812-1. The fourth optical path is substantiallyperpendicular to the light entering the second beam splitter 810-2, andpasses through to the second beam steering element 812-2. In someimplementations, the second beam splitter 810-2 is a 50:50 beamsplitter. In other implementations, the second beam splitter 810-2 is adichroic beam splitter.

The beam steering elements 812 (e.g., 812-1 . . . 812-4) are configuredto change the direction of the light that enters one face of the beamsteering element. Beam steering elements 812 are any appropriate opticaldevice that changes the direction of light. For example, in someimplementations, the beam steering elements 812 are prisms (e.g.,folding prisms or bending prisms). In some implementations, the beamsteering elements 812 are mirrors. In some implementations, the beamsteering elements 812 are other appropriate optical devices orcombinations of devices.

Returning to FIG. 8, the first beam steering element 812-1 is adjacentto and in direct optical communication with the second beam splitter810-2, and receives light from the third optical path (e.g., the outputof the second beam splitter 810-2 that is collinear with the input tothe second beam splitter 810-2). The first beam steering element 812-1deflects the light in a direction that is substantially perpendicular tothe fourth optical path (and, in some implementations, perpendicular toa plane defined by the optical paths of the beam splitters 212, e.g.,the x-y plane) and onto the first photo-sensor 112-1. The output of thefirst beam steering element 214-1 is represented by arrow 31-1.

The second beam steering element 812-2 is adjacent to and in directoptical communication with the second beam splitter 810-2, and receiveslight from the fourth optical path (e.g., the perpendicular output ofthe second beam splitter 810-2). The second beam steering element 812-2deflects the light in a direction that is substantially perpendicular tothe third optical path (and, in some implementations, perpendicular to aplane defined by the optical paths of the beam splitters 810, e.g., thex-y plane) and onto the second photo-sensor 112-2. The output of thesecond beam steering element 812-2 is represented by arrow 31-2.

As noted above, the first beam splitter 810-1 passes light to the secondbeam splitter 810-2 along a first optical path (as discussed above), andto the third beam splitter 810-3 along a second optical path.

The third beam splitter 810-3 is adjacent to the first beam splitter810-1 (and is in direct optical communication with the first beamsplitter 810-1), and splits the incoming light from the first beamsplitter 810-1 into a fifth optical path and a sixth optical path. Thefifth optical path is substantially collinear with the light enteringthe third beam splitter 810-3, and passes through to the third beamsteering element 812-3. The sixth optical path is substantiallyperpendicular to the light entering the third beam splitter 810-3, andpasses through to the fourth beam steering element 812-4. In someimplementations, the third beam splitter 810-3 is a 50:50 beam splitter.In other implementations, the third beam splitter 810-3 is a dichroicbeam splitter.

The third beam steering element 812-3 is adjacent to and in directoptical communication with the third beam splitter 810-3, and receiveslight from the fifth optical path (e.g., the output of the third beamsplitter 810-3 that is collinear with the input to the third beamsplitter 810-3). The third beam steering element 812-3 deflects thelight in a direction that is substantially perpendicular to the thirdoptical path (and, in some implementations, perpendicular to a planedefined by the optical paths of the beam splitters 810, e.g., the x-yplane) and onto the third photo-sensor 112-3. The output of the thirdbeam steering element 812-3 is represented by arrow 31-3.

The fourth beam steering element 812-4 is adjacent to and in directoptical communication with the third beam splitter 810-3, and receiveslight from the sixth optical path (e.g., the perpendicular output of thethird beam splitter 810-3). The fourth beam steering element 812-4deflects the light in a direction that is substantially perpendicular tothe sixth optical path (and, in some implementations, perpendicular to aplane defined by the optical paths of the beam splitters 810, e.g., thex-y plane) and onto the fourth photo-sensor 112-4. The output of thefourth beam steering element 812-4 is represented by arrow 31-4.

As shown in FIG. 8, the output paths of the first and third beamsteering elements 812-1, 812-3 are in opposite directions than theoutput paths of the second and fourth beam steering elements 812-2,812-4. Thus, the image captured by the lens assembly 520 is projectedonto the photo-sensors mounted on the opposite sides of the imageassembly. However, the beam steering elements 812 need not face theseparticular directions. Rather, any of the beam steering elements 812 canbe positioned to direct the output path of each beam steering element812 in any appropriate direction. For example, in some implementations,all of the beam steering elements 812 direct light in the samedirection. In such cases, all of the photo-sensors may be mounted on asingle circuit board. Alternatively, in some implementations, one ormore of the beam steering elements 812 directs light substantiallyperpendicular to the incoming light, but in substantially the same planedefined by the optical paths of the beam splitters 810 (e.g., within thex-y plane).

Further implementations of suitable devices and strategies forcollection images in accordance with the current disclosure aredisclosed in U.S. Non-Provisional application Ser. No. 14/664,754, filedon Mar. 20, 2015, the content of which is expressly incorporated hereinby reference, in its entirety, for all purposes.

Hyperspectral Imaging

Hyperspectral and multispectral imaging are related techniques in largerclass of spectroscopy commonly referred to as spectral imaging orspectral analysis. Typically, hyperspectral imaging relates to theacquisition of a plurality of images, each image representing a narrowspectral band captured over a continuous spectral range, for example, 5or more (e.g., 5, 10, 15, 20, 25, 30, 40, 50, or more) spectral bandshaving a FWHM bandwidth of 1 nm or more each (e.g., 1 nm, 2 nm, 3 nm, 4nm, 5 nm, 10 nm, 20 nm or more), covering a contiguous spectral range(e.g., from 400 nm to 800 nm). In contrast, multispectral imagingrelates to the acquisition of a plurality of images, each imagerepresenting a narrow spectral band captured over a discontinuousspectral range.

For the purposes of the present disclosure, the terms “hyperspectral”and “multispectral” are used interchangeably and refer to a plurality ofimages, each image representing a narrow spectral band (having a FWHMbandwidth of between 10 nm and 30 nm, between 5 nm and 15 nm, between 5nm and 50 nm, less than 100 nm, between 1 and 100 nm, etc.), whethercaptured over a continuous or discontinuous spectral range. For example,in some implementations, wavelengths 1-N of a hyperspectral data cube1336-1 are contiguous wavelengths or spectral bands covering acontiguous spectral range (e.g., from 400 nm to 800 nm). In otherimplementations, wavelengths 1-N of a hyperspectral data cube 1336-1 arenon-contiguous wavelengths or spectral bands covering a non-contiguousspectral ranges (e.g., from 400 nm to 440 nm, from 500 nm to 540 nm,from 600 nm to 680 nm, and from 900 to 950 nm).

As used herein, “narrow spectral range” refers to a continuous span ofwavelengths, typically consisting of a FWHM spectral band of no morethan about 100 nm. In certain embodiments, narrowband radiation consistsof a FWHM spectral band of no more than about 75 nm, 50 nm, 40 nm, 30nm, 25 nm, 20 nm, 15 nm, 10 nm, 5 nm, 4 nm, 3 nm, 2 nm, 1 nm, or less.In some implementations, wavelengths imaged by the methods and devicesdisclosed herein are selected from one or more of the visible,near-infrared, short-wavelength infrared, mid-wavelength infrared,long-wavelength infrared, and ultraviolet (UV) spectrums.

By “broadband” it is meant light that includes component wavelengthsover a substantial portion of at least one band, e.g., over at least20%, or at least 30%, or at least 40%, or at least 50%, or at least 60%,or at least 70%, or at least 80%, or at least 90%, or at least 95% ofthe band, or even the entire band, and optionally includes componentwavelengths within one or more other bands. A “white light source” isconsidered to be broadband, because it extends over a substantialportion of at least the visible band. In certain embodiments, broadbandlight includes component wavelengths across at least 100 nm of theelectromagnetic spectrum. In other embodiments, broadband light includescomponent wavelengths across at least 150 nm, 200 nm, 250 nm, 300 nm,400 nm, 500 nm, 600 nm, 700 nm, 800 nm, or more of the electromagneticspectrum.

By “narrowband” it is meant light that includes components over only anarrow spectral region, e.g., less than 20%, or less than 15%, or lessthan 10%, or less than 5%, or less than 2%, or less than 1%, or lessthan 0.5% of a single band. Narrowband light sources need not beconfined to a single band, but can include wavelengths in multiplebands. A plurality of narrowband light sources may each individuallygenerate light within only a small portion of a single band, buttogether may generate light that covers a substantial portion of one ormore bands, e.g., may together constitute a broadband light source. Incertain embodiments, broadband light includes component wavelengthsacross no more than 100 nm of the electromagnetic spectrum (e.g., has aspectral bandwidth of no more than 100 nm). In other embodiments,narrowband light has a spectral bandwidth of no more than 90 nm, 80 nm,75 nm, 70 nm, 60 nm, 50 nm, 40 nm, 30 nm, 25 nm, 20 nm, 15 nm, 10 nm, 5nm, or less of the electromagnetic spectrum.

As used herein, the “spectral bandwidth” of a light source refers to thespan of component wavelengths having an intensity that is at least halfof the maximum intensity, otherwise known as “full width at halfmaximum” (FWHM) spectral bandwidth. Many light emitting diodes (LEDs)emit radiation at more than a single discreet wavelength, and are thusnarrowband emitters. Accordingly, a narrowband light source can bedescribed as having a “characteristic wavelength” or “centerwavelength,” i.e., the wavelength emitted with the greatest intensity,as well as a characteristic spectral bandwidth, e.g., the span ofwavelengths emitted with an intensity of at least half that of thecharacteristic wavelength.

By “coherent light source” it is meant a light source that emitselectromagnetic radiation of a single wavelength in phase. Thus, acoherent light source is a type of narrowband light source with aspectral bandwidth of less than 1 nm. Non-limiting examples of coherentlight sources include lasers and laser-type LEDs. Similarly, anincoherent light source emits electromagnetic radiation having aspectral bandwidth of more than 1 nm and/or is not in phase. In thisregard, incoherent light can be either narrowband or broadband light,depending on the spectral bandwidth of the light.

Examples of suitable broadband light sources 104 include, withoutlimitation, incandescent lights such as a halogen lamp, xenon lamp, ahydrargyrum medium-arc iodide lamp, and a broadband light emitting diode(LED). In some embodiments, a standard or custom filter is used tobalance the light intensities at different wavelengths to raise thesignal level of certain wavelength or to select for a narrowband ofwavelengths. Broadband illumination of a subject is particularly usefulwhen capturing a color image of the subject or when focusing thehyperspectral/multispectral imaging system.

Examples of suitable narrowband, incoherent light sources 104 include,without limitation, a narrow band light emitting diode (LED), asuperluminescent diode (SLD) (see, Redding B., arVix: 1110.6860 (2011),the content of which is hereby incorporated herein by reference in itsentirety for all purposes), a random laser, and a broadband light sourcecovered by a narrow band-pass filter. Examples of suitable narrowband,coherent light sources 104 include, without limitation, lasers andlaser-type light emitting diodes. While both coherent and incoherentnarrowband light sources 104 can be used in the imaging systemsdescribed herein, coherent illumination is less well suited forfull-field imaging due to speckle artifacts that corrupt image formation(see, Oliver, B. M., Proc IEEE 51, 220-221 (1963)).

The conventional HSI system involves two scanning methods: spatialscanning and spectral scanning Spatial scanning methods generatehyperspectral images by acquiring a complete spectrum for each pixel inthe case of whiskbroom (point-scanning) instruments or line of pixels inpushbroom (line-scanning) instruments, and then spatially scanningthrough the scene. Spectral scanning methods, also called staring orarea-scanning imaging, involves capturing the whole scene with 2-Ddetector arrays in a single exposure and then stepping throughwavelengths to complete the data cube.

Hyperspectral Medical Imaging

The disclosure provides systems and methods useful forhyperspectral/multispectral medical imaging (HSMI). HSMI relies upondistinguishing the interactions that occur between light at differentwavelengths and components of the human body, especially componentslocated in or just under the skin. For example, it is well known thatdeoxyhemoglobin absorbs a greater amount of light at 700 nm than doeswater, while water absorbs a much greater amount of light at 1200 nm, ascompared to deoxyhemoglobin. By measuring the absorbance of atwo-component system consisting of deoxyhemoglobin and water at 700 nmand 1200 nm, the individual contribution of deoxyhemoglobin and water tothe absorption of the system, and thus the concentrations of bothcomponents, can readily be determined. By extension, the individualcomponents of more complex systems (e.g., human skin) can be determinedby measuring the absorption of a plurality of wavelengths of lightreflected or backscattered off of the system.

The particular interactions between the various wavelengths of lightmeasured by hyperspectral/multispectral imaging and each individualcomponent of the system (e.g., skin) produceshyperspectral/multispectral signature, when the data is constructed intoa hyperspectral/multispectral data cube. Specifically, different regions(e.g., different ROI on a single subject or different ROI from differentsubjects) interact differently with light depending on the presence of,e.g., a medical condition in the region, the physiological structure ofthe region, and/or the presence of a chemical in the region. Forexample, fat, skin, blood, and flesh all interact with variouswavelengths of light differently from one another. A given type ofcancerous lesion interacts with various wavelengths of light differentlyfrom normal skin, from non-cancerous lesions, and from other types ofcancerous lesions. Likewise, a given chemical that is present (e.g., inthe blood, or on the skin) interacts with various wavelengths of lightdifferently from other types of chemicals. Thus, the light obtained fromeach illuminated region of a subject has a spectral signature based onthe characteristics of the region, which signature contains medicalinformation about that region.

The structure of skin, while complex, can be approximated as twoseparate and structurally different layers, namely the epidermis anddermis. These two layers have very different scattering and absorptionproperties due to differences of composition. The epidermis is the outerlayer of skin. It has specialized cells called melanocytes that producemelanin pigments. Light is primarily absorbed in the epidermis, whilescattering in the epidermis is considered negligible. For furtherdetails, see G. H. Findlay, “Blue Skin,” British Journal of Dermatology83(1), 127-134 (1970), the content of which is incorporated herein byreference in its entirety for all purposes.

The dermis has a dense collection of collagen fibers and blood vessels,and its optical properties are very different from that of theepidermis. Absorption of light of a bloodless dermis is negligible.However, blood-born pigments like oxy- and deoxy-hemoglobin and waterare major absorbers of light in the dermis. Scattering by the collagenfibers and absorption due to chromophores in the dermis determine thedepth of penetration of light through skin.

Light used to illuminate the surface of a subject will penetrate intothe skin. The extent to which the light penetrates will depend upon thewavelength of the particular radiation. For example, with respect tovisible light, the longer the wavelength, the farther the light willpenetrate into the skin. For example, only about 32% of 400 nm violetlight penetrates into the dermis of human skin, while greater than 85%of 700 nm red light penetrates into the dermis or beyond (see, CapineraJ. L., Encyclopedia of Entomology, 2nd Edition, Springer Science (2008)at page 2854, the content of which is hereby incorporated herein byreference in its entirety for all purposes). For purposes of the presentdisclosure, when referring to “illuminating a tissue,” “reflecting lightoff of the surface,” and the like, it is meant that radiation of asuitable wavelength for detection is backscattered from a tissue of asubject, regardless of the distance into the subject the light travels.For example, certain wavelengths of infra-red radiation penetrate belowthe surface of the skin, thus illuminating the tissue below the surfaceof the subject.

Briefly, light from the illuminator(s) on the systems described hereinpenetrates the subject's superficial tissue and photons scatter in thetissue, bouncing inside the tissue many times. Some photons are absorbedby oxygenated hemoglobin molecules at a known profile across thespectrum of light. Likewise for photons absorbed by de-oxygenatedhemoglobin molecules. The images resolved by the optical detectorsconsist of the photons of light that scatter back through the skin tothe lens subsystem. In this fashion, the images represent the light thatis not absorbed by the various chromophores in the tissue or lost toscattering within the tissue. In some embodiments, light from theilluminators that does not penetrate the surface of the tissue iseliminated by use of polarizers. Likewise, some photons bounce off thesurface of the skin into air, like sunlight reflecting off a lake.

Accordingly, different wavelengths of light may be used to examinedifferent depths of a subject's skin tissue. Generally, high frequency,short-wavelength visible light is useful for investigating elementspresent in the epidermis, while lower frequency, long-wavelength visiblelight is useful for investigating both the epidermis and dermis.Furthermore, certain infra-red wavelengths are useful for investigatingthe epidermis, dermis, and subcutaneous tissues.

In the visible and near-infrared (VNIR) spectral range and at lowintensity irradiance, and when thermal effects are negligible, majorlight-tissue interactions include reflection, refraction, scattering andabsorption. For normal collimated incident radiation, the regularreflection of the skin at the air-tissue interface is typically onlyaround 4%-7% in the 250-3000 nanometer (nm) wavelength range. Forfurther details, see R. R. Anderson and J. A. Parrish, “The optics ofhuman skin,” Journal of Investigative Dermatology 77(1), 13-19 (1981),the content of which is hereby incorporated by reference in its entiretyfor all purposes. When neglecting the air-tissue interface reflectionand assuming total diffusion of incident light after the stratum corneumlayer, the steady state VNIR skin reflectance can be modeled as thelight that first survives the absorption of the epidermis, then reflectsback toward the epidermis layer due the isotropic scattering in thedermis layer, and then finally emerges out of the skin after goingthrough the epidermis layer again.

Accordingly, the systems and methods described herein can be used todiagnose and characterize a wide variety of medical conditions. In oneembodiment, the concentration of one or more skin or blood component isdetermined in order to evaluate a medical condition in a patient.Non-limiting examples of components useful for medical evaluationinclude: deoxyhemoglobin levels, oxyhemoglobin levels, total hemoglobinlevels, oxygen saturation, oxygen perfusion, hydration levels, totalhematocrit levels, melanin levels, collagen levels, and bilirubinlevels. Likewise, the pattern, gradient, or change over time of a skinor blood component can be used to provide information on the medicalcondition of the patient.

Non-limiting examples of conditions that can be evaluated byhyperspectral/multispectral imaging include: tissue ischemia, ulcerformation, ulcer progression, pressure ulcer formation, pressure ulcerprogression, diabetic foot ulcer formation, diabetic foot ulcerprogression, venous stasis, venous ulcer disease, peripheral arterydisease, atherosclerosis, infection, shock, cardiac decompensation,respiratory insufficiency, hypovolemia, the progression of diabetes,congestive heart failure, sepsis, dehydration, hemorrhage, hemorrhagicshock, hypertension, cancer (e.g., detection, diagnosis, or typing oftumors or skin lesions), retinal abnormalities (e.g., diabeticretinopathy, macular degeneration, or corneal dystrophy), skin wounds,burn wounds, exposure to a chemical or biological agent, and aninflammatory response.

In one embodiment, the systems and methods described herein are used toevaluate tissue oximetery and correspondingly, medical conditionsrelating to patient health derived from oxygen measurements in thesuperficial vasculature. In certain embodiments, the systems and methodsdescribed herein allow for the measurement of oxygenated hemoglobin,deoxygenated hemoglobin, oxygen saturation, and oxygen perfusion.Processing of these data provide information to assist a physician with,for example, diagnosis, prognosis, assignment of treatment, assignmentof surgery, and the execution of surgery for conditions such as criticallimb ischemia, diabetic foot ulcers, pressure ulcers, peripheralvascular disease, surgical tissue health, etc.

In one embodiment, the systems and methods described herein are used toevaluate diabetic and pressure ulcers. Development of a diabetic footulcer is commonly a result of a break in the barrier between the dermisof the skin and the subcutaneous fat that cushions the foot duringambulation. This rupture can lead to increased pressure on the dermis,resulting in tissue ischemia and eventual death, and ultimatelymanifesting in the form of an ulcer (Frykberg R. G. et al., DiabetesCare 1998; 21(10):1714-9). Measurement of oxyhemoglobin,deoxyhemoglobin, and/or oxygen saturation levels byhyperspectral/multispectral imaging can provide medical informationregarding, for example: a likelihood of ulcer formation at an ROI,diagnosis of an ulcer, identification of boundaries for an ulcer,progression or regression of ulcer formation, a prognosis for healing ofan ulcer, the likelihood of amputation resulting from an ulcer. Furtherinformation on hyperspectral/multispectral methods for the detection andcharacterization of ulcers, e.g., diabetic foot ulcers, are found inU.S. Patent Application Publication No. 2007/0038042, and Nouvong A. etal., Diabetes Care. 2009 November; 32(11):2056-61, the contents of whichare hereby incorporated herein by reference in their entireties for allpurposes.

Other examples of medical conditions include, but are not limited to:tissue viability (e.g., whether tissue is dead or living, and/or whetherit is predicted to remain living); tissue ischemia; malignant cells ortissues (e.g., delineating malignant from benign tumors, dysplasias,precancerous tissue, metastasis); tissue infection and/or inflammation;and/or the presence of pathogens (e.g., bacterial or viral counts). Someembodiments include differentiating different types of tissue from eachother, for example, differentiating bone from flesh, skin, and/orvasculature. Some embodiments exclude the characterization ofvasculature.

In yet other embodiments, the systems and methods provided herein can beused during surgery, for example to determine surgical margins, evaluatethe appropriateness of surgical margins before or after a resection,evaluate or monitor tissue viability in near-real time or real-time, orto assist in image-guided surgery. For more information on the use ofhyperspectral/multispectral imaging during surgery, see, Holzer M. S. etal., J Urol. 2011 August; 186(2):400-4; Gibbs-Strauss S. L. et al., MolImaging. 2011 April; 10(2):91-101; and Panasyuk S. V. et al., CancerBiol Ther. 2007 March; 6(3):439-46, the contents of which are herebyincorporated herein by reference in their entirety for all purposes.

For more information on the use of hyperspectral/multispectral imagingin medical assessments, see, for example: Chin J. A. et al., J VascSurg. 2011 December; 54(6):1679-88; Khaodhiar L. et al., Diabetes Care2007; 30:903-910; Zuzak K. J. et al., Anal Chem. 2002 May 1;74(9):2021-8; Uhr J. W. et al., Transl Res. 2012 May; 159(5):366-75;Chin M. S. et al., J Biomed Opt. 2012 February; 17(2):026010; Liu Z. etal., Sensors (Basel). 2012; 12(1):162-74; Zuzak K. J. et al., Anal Chem.2011 Oct. 1; 83(19):7424-30; Palmer G. M. et al., J Biomed Opt. 2010November-December; 15(6):066021; Jafari-Saraf and Gordon, Ann Vasc Surg.2010 August; 24(6):741-6; Akbari H. et al., IEEE Trans Biomed Eng. 2010August; 57(8):2011-7; Akbari H. et al., Conf Proc IEEE Eng Med Biol Soc.2009:1461-4; Akbari H. et al., Conf Proc IEEE Eng Med Biol Soc.2008:1238-41; Chang S. K. et al., Clin Cancer Res. 2008 Jul. 1;14(13):4146-53; Siddiqi A. M. et al., Cancer. 2008 Feb. 25;114(1):13-21; Liu Z. et al., Appl Opt. 2007 Dec. 1; 46(34):8328-34; ZhiL. et al., Comput Med Imaging Graph. 2007 December; 31(8):672-8;Khaodhiar L. et al., Diabetes Care. 2007 April; 30(4):903-10; Ferris D.G. et al., J Low Genit Tract Dis. 2001 April; 5(2):65-72; Greenman R. L.et al., Lancet. 2005 Nov. 12; 366(9498):1711-7; Sorg B. S. et al., JBiomed Opt. 2005 July-August; 10(4):44004; Gillies R. et al., andDiabetes Technol Ther. 2003; 5(5):847-55, the contents of which arehereby incorporated herein by reference in their entirety for allpurposes.

In yet other embodiments, the systems and methods provided herein can beused during surgery, for example to determine surgical margins, evaluatethe appropriateness of surgical margins before or after a resection,evaluate or monitor tissue viability in near-real time or real-time, orto assist in image-guided surgery. For more information on the use ofhyperspectral/multispectral imaging during surgery, see, Holzer M. S. etal., J Urol. 2011 August; 186(2):400-4; Gibbs-Strauss S. L. et al., MolImaging. 2011 April; 10(2):91-101; and Panasyuk S. V. et al., CancerBiol Ther. 2007 March; 6(3):439-46, the contents of which are herebyincorporated herein by reference in their entirety for all purposes.

EXAMPLES Example 1 Selection of Wavelengths for Tissue OxygenationMeasurements by Sensitivity Maximization

An initial attempt was made to select a minimal set of eight wavelengthsthat allow accurate determination of tissue oxygenation in human tissue,by selecting wavelengths that optimized sensitivity to oxyhemoglobin anddeoxyhemoglobin (e.g., the chromophores of interest), while minimizingsensitivity to melanin (e.g., the major background chromophore insurface tissues). Numerical optimization is used to select a set ofwavelengths, defined as {right arrow over (λ)}, that ideally maximizesthe ratio:

$\begin{matrix}{L = \frac{\frac{dOXY}{d\; c_{oxy}} \cdot \frac{dDEOXY}{d\; c_{deoxy}}}{\frac{dOXY}{d\; c_{oxy}} \cdot \frac{dDEOXY}{d\; c_{deoxy}} \cdot \frac{dOXY}{d\; c_{melanin}} \cdot \frac{dDEOXY}{d\; c_{melanin}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where dOXY is change in measured oxyhemoglobin concentration, dDEOXY ischange in measured deoxyhemoglobin concentration, dc_(oxy) is change inoxyhemoglobin concentration, dc_(deoxy) is change in deoxyhemoglobinconcentration, and dc_(melanin) is change in melanin concentration. Theproper set of wavelengths {right arrow over (λ)} will increase thesensitivity of the measured OXY/DEOXY to the true concentration ofoxyhemoglobin c_(oxy)/c_(deoxy) and minimize the cross-sensitivitybetween OXY and DEOXY and their sensitivity to the melanin concentrationc_(melanin). Potential members of {right arrow over (λ)} were restrictedto be between 500 and 640 nm and to be multiples of 5 nm. However, inpractice, the expression could be evaluated over any range ofwavelengths and relative wavelength steps.

To evaluate L for a given candidate set of wavelengths {right arrow over(λ)}, the tissue reflectance for wavelength set {right arrow over (λ)}was simulated using a range of randomized concentrations c_(oxy),c_(deoxy), and c_(melanin). The hyperspectral algorithm foroxyhemoglobin and deoxyhemoglobin determination provided in U.S. Pat.No. 8,644,911 was then applied to the simulated tissue reflectances toestimate OXY and DEOXY. The derivatives in Equation 1 were approximatedby perturbing c_(oxy), c_(deoxy), and c_(melanin) by a small change inconcentration (˜10⁻⁶) and estimating the perturbed OXY and DEOXY fromthe simulated tissue reflectance of the perturbed concentrations.

For perturbed concentrations of oxyhemoglobin, deoxyhemoglobin, andmelanin c_(oxy,p), c_(deoxy,p), and c_(melanin,p)), and perturbed OXYand DEOXY estimates (OXY_(p) and DEOXy_(p)), the sensitivity ofOXY/DEOXY is approximated by:

$\begin{matrix}{\frac{dOXY}{d\; c_{oxy}} = \frac{{OXY} - {OXY}_{p}}{c_{oxy} - c_{{oxy},p}}} & {{Equation}\mspace{14mu} 2} \\{\frac{dDEOXY}{d\; c_{oxy}} = \frac{{DEOXY} - {DEOXY}_{p}}{c_{deoxy} - c_{{deoxy},p}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

the cross-sensitivity between OXY and DEOXY is approximated by:

$\begin{matrix}{\frac{dOXY}{d\; c_{deoxy}} = \frac{{OXY} - {OXY}_{p}}{c_{deoxy} - c_{{deoxy},p}}} & {{Equation}\mspace{14mu} 4} \\{\frac{dDEOXY}{d\; c_{oxy}} = \frac{{DEOXY} - {DEOXY}_{p}}{c_{deoxy} - c_{{oxy},p}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

and the sensitivity to melanin is approximated by:

$\begin{matrix}{\frac{dOXY}{d\; c_{melanin}} = \frac{{OXY} - {OXY}_{p}}{c_{melanin} - c_{{melanin},p}}} & {{Equation}\mspace{14mu} 6} \\{\frac{dDEOXY}{d\; c_{melanin}} = \frac{{DEOXY} - {DEOXY}_{p}}{c_{melanin} - c_{{melanin},p}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

L was found by evaluating Equation 2 to Equation 7 and substituting thesolutions into Equation 1. In each case, c_(x,p)=c_(x)+10⁻⁶. The medianvalue of L was found over randomized sets of c_(oxy), c_(deoxy), andC_(melanin) at a fixed {right arrow over (λ)}. The process was repeatedusing different {right arrow over (λ)} chosen by a guided exhaustivesearch (e.g., using a genetic algorithm) until a {right arrow over (λ)}was found that maximized L. The final set of selected wavelengths fromthe analysis was 500 nm, 530 nm, 545 nm, 570 nm, 585 nm, 600 nm, 615 nm,and 640 nm.

Example 2 Selection of Wavelengths for Tissue Oxygenation Measurementsby Exhaustive Search Over Clinical Data

The objective of the search was to find sets of eight wavelengths thatperform about as well in determining tissue oxygenation as the set offifteen wavelengths described in U.S. Pat. No. 8,644,911 (500 nm, 510nm, 520 nm, 530 nm, 540 nm, 550 nm, 560 nm, 570 nm, 580 nm, 590 nm, 600nm, 610 nm, 620 nm, 640 nm, and 660 nm), the disclosure of which ishereby expressly incorporated by reference in its entirety for allpurposes.

A reference dataset containing 169 hypercubes (i.e., image sets capturedat each of the fifteen wavelengths disclosed above) from approximately50 health patients was used for this analysis. Briefly, each hypercubewas processed according to the hyperspectral algorithm disclosed in U.S.Pat. No. 8,644,911, at all fifteen wavelengths, to determine baselineoxyhemoglobin and deoxyhemoglobin values for each pixel. The algorithmwas then applied to the same hypercube, at a unique subset of eightwavelengths. The resulting OXY and DEOXY maps for each subset of eightwavelengths was then compared to the baseline oxyhemoglobin anddeoxyhemoglobin values determined using all fifteen wavelengths. Thefifteen and eight wavelength processed maps were split into averagedsegments, compared, and their correlation evaluated. The process wasperformed for all 6435 combinations of eight wavelengths from theoriginal set of fifteen wavelengths.

OXY and DEOXY maps were produced from a set of measured reflectancemaps, R_(measured) (λ), at multiple wavelengths by converting tissuereflectance into apparent absorption and estimating the relativeoxyhemoglobin and deoxyhemoglobin concentrations. The relationshipbetween measured reflectance and OXY and DEOXY is described by:

R ⁻¹(A(λ))→(OXY,DEOXY)  Equation 8

where A(λ) is the apparent absorption defined as:

A(λ)=−log₁₀(R _(measured)(λ))  Equation 9

where R_(measured) (λ) is the wavelength dependent reflectance image oftissue captured by the hyperspectral imaging device.

The transformation from reflectance to apparent absorption and torelative concentration arises from the Beer-Lambert law which positsthat the intensity of light traveling through an absorbing butnon-scattering medium decays exponentially with the product of thedistance traveled and absorption coefficient of the medium.

In the simple case of light travelling through a non-scattering media ina cuvette, light with initial intensity I₀ and output intensity I₁,incident to a cuvette of length L filled with a mixture of substances,results in a total absorption coefficient of μ_(a)=ΣC_(i)ε_(i).Absorption A(λ) is logarithmically related to the ratio of the intensityI₀ and output intensity I₁, also called transmittance

${T = \frac{I_{1}}{I_{0}}},$

and is linearly related to the absorption spectra ε_(i)(λ) of theindividual chromophores in the cuvette via their molar concentrationsC_(i), defined as:

$\begin{matrix}{{A(\lambda)} = {{- {\log_{10}\left( \frac{I_{1}(\lambda)}{I_{0}(\lambda)} \right)}} = {{L{\sum C_{i}}} \in_{i}(\lambda)}}} & {{Equation}\mspace{14mu} 10}\end{matrix}$

Since the cuvette side-length L and the molar absorption spectra ε_(i)(λ) are known, the concentrations C_(i) can be found by linearly leastsquares fitting.

However, light traversing tissue encounters multiple absorption andscattering events before exiting the tissue, and thus requires the useof a modified Beer-Lambert law described as:

A(λ)=−log₁₀(R _(measured)(λ))=L(μ′_(s)(λ))ΣC _(i)ε_(i)(λ)  Equation 11

where the function L(μ′_(s)(λ)) is the effective average path-length oflight through tissue before remittance and is a function of thescattering properties of the tissue and the wavelength of light.L(μ′_(s)(λ)) can be simplified into a constant by averaging over thewavelengths of interest defined by:

$\begin{matrix}{\overset{\sim}{L} = {\frac{1}{\lambda_{\max} - \lambda_{\min}}{\int_{\lambda_{\min}}^{\lambda_{\max}}{{L\left( {\mu_{s}^{\prime}(\lambda)} \right)}{\lambda}}}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

Then the modified Beer-Lambert law simplifies into an equation definingthe relationship between apparent absorption and relative concentration

A(λ)≈{tilde over (L)}ΣC _(i)ε_(i)(λ)=Σk _(i)ε_(i)(λ)  Equation 13

The simplified and modified Beer-Lambert law is similar to Equation 10in that C_(i) and k_(i) are solvable through linear least square fit.Importantly, the concentration of a chromophore C_(i) is proportional torelative concentrations k_(i) and that exact knowledge of L(μ′_(s)(λ))and {tilde over (L)} is not required if only relative concentrations areof interest.

Applying the measured reflectance from each candidate subset of eightwavelengths to Equation 13, the apparent absorption was modeled as alinear combination of:

A(λ)=k ₁ +k ₂ λ+k ₃λ² +k ₄ε_(oxy)(λ)+k ₅ε_(deoxy)(λ)  Equation 14

where δ_(oxy)(λ) and ε_(deoxy)(λ) are the molar absorption coefficientsof oxyhemoglobin and deoxyhemoglobin, k₄ and k₅ are the relativeconcentrations of oxyhemoglobin and deoxyhemoglobin, and is thewavelength of light interrogating the tissue. The constant k₁, lineark₂, and quadratic k₃ are present in the linear combination to accountfor absorption contribution by melanin. Each k_(i) was solved by linearleast square fit.

Finally, relative concentrations of oxyhemoglobin and deoxyhemoglobinwere converted to OXY and DEOXY by:

OXY=c _(oxy) c _(scale) k ₄  Equation 15

DEOXY=c _(deoxy) c _(scale) k ₅  Equation 16

where c_(oxy) and c_(deoxy) are scaling factors determined empiricallyfrom correlation experiments and C_(scale) is an arbitrary scaleconstant chosen for esthetic purposes.

Conventionally, oxyhemoglobin and deoxyhemoglobin values determined bymedical hyperspectral imaging are presented to the physician as averagesover a subset of the pixels in an image. Thus, to assess the accuracy ofthe candidate subsets of eight wavelengths, as compared to the fifteenwavelength standard, average OXY and DEOXY values were averaged overcontiguous squares of approximately forty by forty pixels. The resultingaverages of OXY and DEOXY determined using sets of eight and fifteenwavelengths were compared for each hypercube. An example of the squaresegmentation for an OXY map is shown in FIG. 9.

To improve the accuracy of oxyhemoglobin and deoxyhemoglobin measurementusing eight wavelengths, a linear correction was applied to correlatethe results with those achieved using fifteen wavelengths. The linearcorrection for OXY and DEOXY were solved by fitting observations of OXYand DEOXY into the linear models:

$\begin{matrix}{\begin{bmatrix}{OXY}_{15,1} \\{OXY}_{15,2} \\{OXY}_{15,3} \\\vdots \\{OXY}_{15,n}\end{bmatrix} = {\begin{bmatrix}1 & {OXY}_{8,1} & {DEOXY}_{8,1} \\1 & {OXY}_{8,2} & {DEOXY}_{8,2} \\1 & {OXY}_{8,3} & {DEOXY}_{8,3} \\\vdots & \vdots & \vdots \\1 & {OXY}_{8,n} & {DEOXY}_{8,n}\end{bmatrix}\begin{bmatrix}{oc}_{1} \\{oc}_{2} \\{oc}_{3}\end{bmatrix}}} & {{Equation}\mspace{14mu} 17} \\{\begin{bmatrix}{DEOXY}_{15,1} \\{DEOXY}_{15,2} \\{DEOXY}_{15,3} \\\vdots \\{DEOXY}_{15,n}\end{bmatrix} = {\begin{bmatrix}1 & {OXY}_{8,1} & {DEOXY}_{8,1} \\1 & {OXY}_{8,2} & {DEOXY}_{8,2} \\1 & {OXY}_{8,3} & {DEOXY}_{8,3} \\\vdots & \vdots & \vdots \\1 & {OXY}_{8,n} & {DEOXY}_{8,n}\end{bmatrix}\begin{bmatrix}{d\; c_{1}} \\{d\; c_{2}} \\{d\; c_{3}}\end{bmatrix}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$

where OXY_(15,n) and DEOXY_(15,n) are the averaged values of 15wavelength results, OXY_(8,1) and DEOXY_(8,1) are the averaged values of8 wavelength results. The value of n was n=9×9×169=13,689 andcorresponded to the total number of averaging squares over 169 imagestaken from the normal population. The values oc₁, oc₂, and oc₃ arelinear correction coefficients for OXY, and dc₁, dc₂, and dc₃ are linearcorrection coefficients for DEOXY. The coefficients oc and dc can besolved using linear least square fit. Substituting the coefficients intoEquation 19 and Equation 20 enables evaluating corrected OXY and DEOXYvalues

OXY_(corrected) =oc ₁ +oc ₂OXY₈ +oc ₃DEOXY₈  Equation 19

DEOXY_(corrected) =dc ₁ +dc ₂OXY₈ +dc ₃DEOXY₈  Equation 20

The corrected OXY and DEOXY values generated from candidate subsets ofeight wavelengths were then evaluated against the OXY and DEOXY valuesgenerated using all fifteen wavelengths by fitting a scatter plot to astatistical model and computing the coefficient of determination (R²)for all subsets of eight wavelengths. R² values close to 1 are desiredand indicate a good fit. R² can be evaluated by:

$\begin{matrix}{R^{2} = \frac{\sum_{i}^{I}\left( {y_{i} - f_{i}} \right)^{2}}{\sum_{i}^{I}\left( {y_{i} - \overset{\_}{y}} \right)^{2}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

where y is the average 8 wavelengths OXY or DEOXY value, y_(i) is anelement of the 8 wavelengths result, and f_(i) is the correspondingelement of the statistical model and subsequently the corresponding 15wavelengths OXY or DEOXY value (namely, the 45 degree line). FIGS.10A-10B illustrate OXY or DEOXY scatter plots for an exemplary subset ofeight wavelenghs, respectively.

Evaluation of the scatter plot comparisons revealed two eight-wavelengthsubsets that provided measurements correlating to both the OXY or DEOXYmeasurements obtained using all fifteen wavelengths with an R² value ofat least 0.99. These subsets are given in Table 1.

TABLE 1 Optimal sets of eight wavelengths. DEOXY Subset ID W1 W2 W3 W4W5 W6 W7 W8 OXY R² R² 1 4526 510 530 540 560 580 590 620 660 0.998 0.9962 5778 520 540 560 580 590 610 620 640 0.994 0.990

The identified wavelengths have near perfect correlation between eightwavelengths and fifteen wavelengths. Notably, the first subset includeswavelengths between 510 nm and 660 nm, while the second subset includeswavelengths between 520 nm and 640 nm. The 30 nm difference between thespan of the first subset and the span of the second subset allow forsome flexibility in the design of a suitable hyperspectral camera.

Example 3 Evaluation of Optimal Subset #2

An individual data set, containing images of the bottom of a foot from ahealthy individual at all fifteen wavelengths, was processed usingeither the full fifteen wavelengths or the eight wavelengths in subset5778 (optimal subset number 2). As shown in FIGS. 11A-11E, there wereminimal visual differences between the processed OXY and DEOXY mapsgenerated using all fifteen wavelengths (FIG. 11A—OXY; FIG. 11C—DEOXY)and those generated using only eight wavelengths (FIG. 11B—OXY; FIG.11D—DEOXY). The OXY and DEOXY maps generated using only eightwavelengths were corrected using the linear correction factors forsubset number 2. FIG. 11E shows a native image of the tissue.

Statistics for the corrected and uncorrected OXY and DEOXY pixel valuesdetermined using eight wavelengths were then plotted against thosedetermined using all fifteen wavelengths. FIGS. 12A and 12C arehistograms showing the pixel value distribution of the three OXY andDEOXY maps, respectively. As shown, the shapes of the histogramsgenerated using pixel data from the eight-wavelength andfifteen-wavelength analysis are similar. FIGS. 12B and 12D are scatterplots of the uncorrected and corrected pixel values determined usingeight wavelengths plotted against pixel values determined using allfifteen wavelengths. As shown, there is minimal difference between thecorrected and uncorrected data generated using eight wavelengths and themean of the data generated with fifteen wavelengths or eight wavelengths(corrected or uncorrected).

Qualitative analysis of the OXY and DEOXY maps generated with fifteenand eight wavelengths was then performed by averaging square segments ofthe maps. FIGS. 13A and 13C show mean pixel values for approximately40-pixel squares overlaid on the OXY and DEOXY maps generated using allfifteen wavelengths. The cross indicates the bottom right of eachsquare. The difference between the averaged values in the maps generatedusing fifteen wavelengths and the corrected maps generated using eightwavelengths was then determined. FIGS. 13B and 13D show the differencebetween the averaged values overlaid on the OXY and DEOXY maps generatedusing the corrected eight wavelengths. Positive difference valuesindicate over-prediction, while negative values indicateunder-prediction. As shown, the error introduced by the use of onlyeight wavelengths is minimal, and is only noticeable along the edges ofthe limb.

Example 4 Further Evaluation of Optimal Subset #2

Two more data sets, containing images of the bottom of healthyindividuals' feet at all fifteen wavelengths, were further was processedusing either the full fifteen wavelengths or the eight wavelengths insubset 5778 (optimal subset number 2). As shown in FIGS. 14A-14E, therewere minimal visual differences between the processed OXY and DEOXY mapsgenerated using all fifteen wavelengths (FIG. 14A—OXY; FIG. 14C—DEOXY)and those generated using only eight wavelengths (FIG. 14B—OXY; FIG.14D—DEOXY) for the first data set. The OXY and DEOXY maps generatedusing only eight wavelengths were corrected using the linear correctionfactors for subset number 2. FIG. 14E shows a native image of thetissue.

Statistics for the corrected and uncorrected OXY and DEOXY pixel valuesdetermined using eight wavelengths were then plotted against thosedetermined using all fifteen wavelengths. FIGS. 15A and 15C arehistograms showing the pixel value distribution of the three OXY andDEOXY maps, respectively. FIGS. 15B and 15D are scatter plots of theuncorrected and corrected pixel values determined using eightwavelengths plotted against pixel values determined using all fifteenwavelengths. As shown in the histograms, the uncorrected and correctedmaps generated using eight wavelengths have higher OXY values and lowerDEOXY values than the wavelength maps generated using all fifteenwavelengths. The scatter plots and mean statistics indicate that themaps generated using eight wavelengths over-predict OXY and slightlyunder-predict DEOXY values, as compared to maps generated using allfifteen wavelengths.

Qualitative analysis of the OXY and DEOXY maps generated with fifteenand eight wavelengths was then performed by averaging square segments ofthe maps. FIGS. 16A and 16C show mean pixel values for approximately40-pixel squares overlaid on the OXY and DEOXY maps generated using allfifteen wavelengths. The cross indicates the bottom right of eachsquare. The difference between the averaged values in the maps generatedusing fifteen wavelengths and the corrected maps generated using eightwavelengths was then determined. FIGS. 16B and 16D show the differencebetween the averaged values overlaid on the OXY and DEOXY maps generatedusing the corrected eight wavelengths. The number of positive errors inthe OXY error map indicates over-prediction of OXY values. The netnumber of negative errors in the DEOXY error map indicatesunder-prediction of DEOXY values.

As shown in FIGS. 17A-17E, there were minimal visual differences betweenthe processed OXY and DEOXY maps generated using all fifteen wavelengths(FIG. 17A—OXY; FIG. 17C—DEOXY) and those generated using only eightwavelengths (FIG. 17B—OXY; FIG. 17D—DEOXY) for the first data set. TheOXY and DEOXY maps generated using only eight wavelengths were correctedusing the linear correction factors for subset number 2. FIG. 17E showsa native image of the tissue.

However, the data generated from the second data set indicate unusualand high variance artifacts from the processing which affects thescatter plot by producing large OXY and DEOXY data points with largevariance. However, as shown in histograms of FIGS. 17A and 17C, theartifacts are relatively constant between the maps processed usingfifteen and eight wavelengths. Consistent with this, the difference inmean OXY and DEOXY between the map generated using eight and fifteenwavelengths is minimal. FIGS. 19B and 19D show the difference betweenthe averaged values overlaid on the OXY and DEOXY maps generated usingthe corrected eight wavelengths.

It will also be understood that, although the terms “first,” “second,”etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first wavelengthcould be termed a second wavelength, and, similarly, a second wavelengthcould be termed a first wavelength, which changing the meaning of thedescription, so long as all occurrences of the “first wavelength” arerenamed consistently and all occurrences of the “second wavelength” arerenamed consistently. The first wavelength and the second wavelength areboth wavelengths, but they are not the same wavelength.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the claims. Asused in the description of the embodiments and the appended claims, thesingular forms “a,” “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for determining tissue oxygenationcomprising: at a system comprising a processor and memory, the memoryincluding instructions for: obtaining a data set comprising a pluralityof images of a tissue of interest, each respective image in theplurality of images resolved at a different spectral band, in apredetermined set of eight to twelve spectral bands, and comprising anarray of pixel values; registering, using the processor, the pluralityof images on a pixel-by-pixel basis, to form a plurality of registeredimages of the tissue; and performing spectral analysis at a plurality ofpoints in a two-dimensional area of the plurality of registered imagesof the tissue, the spectral analysis including determining approximatevalues of oxyhemoglobin levels and deoxyhemoglobin levels at eachrespective point in the plurality of points, wherein the predeterminedset of eight to twelve spectral bands includes spectral bands havingcentral wavelengths of: (i) 510±3 nm, 530±3 nm, 540±3 nm, 560±3 nm,580±3 nm, 590±3 nm, 620±3 nm, and 660±3 nm, (ii) 520±3 nm, 540±3 nm,560±3 nm, 580±3 nm, 590±3 nm, 610±3 nm, 620±3 nm, and 640±3 nm, or (iii)500±3 nm, 530±3 nm, 545±3 nm, 570±3 nm, 585±3 nm, 600±3 nm, 615±3 nm,and 640±3 nm; and wherein each respective spectral band in the eight totwelve spectral bands has a full width at half maximum of less than 15nm.
 2. The method of claim 1, wherein obtaining the data set comprises:at the system: capturing the plurality of images of the tissue ofinterest.
 3. The method of claim 2, wherein all of the plurality ofimages are captured concurrently.
 4. The method of claim 2, wherein afirst subset of the plurality of images is captured concurrently at afirst time point and a second subset of the plurality of images iscaptured concurrently at a second time point, other than the first timepoint.
 5. The method of claim 2, wherein the system is an imaging systemand the spectral analysis is performed at the imaging system.
 6. Themethod of claim 1, wherein performing the spectral analysis comprises:resolving absorption signals at each respective point in the pluralityof points; accounting for a melanin contribution and loss of signal fromdiffuse scattering at each respective point in the plurality of points,thereby forming a plurality of corrected absorption signals; anddetermining approximate values of oxyhemoglobin levels anddeoxyhemoglobin levels from the corrected absorption signals at eachrespective point in the plurality of points.
 7. The method of claim 6,wherein the contribution provided by melanin and the losses provided bydiffuse scattering to the plurality of tissue oxygenation measurementsare collectively modeled as a second order polynomial.
 8. The method ofclaim 1, wherein the predetermined set of eight to twelve spectral bandsconsists of a set of eight spectral bands, wherein the set of eightspectral bands have central wavelengths of: (i) 510±3 nm, 530±3 nm,540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm, 620±3 nm, and 660±3 nm; (ii)520±3 nm, 540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm, 610±3 nm, 620±3 nm,and 640±3 nm; or (iii) 500±3 nm, 530±3 nm, 545±3 nm, 570±3 nm, 585±3 nm,600±3 nm, 615±3 nm, and 640±3 nm.
 9. The method of claim 1, wherein thepredetermined set of eight to twelve spectral bands consists of a set ofeight spectral bands, wherein the set of eight spectral bands havehaving central wavelengths of: (i) 510±2 nm, 530±2 nm, 540±2 nm, 560±2nm, 580±2 nm, 590±2 nm, 620±2 nm, and 660±2 nm; (ii) 520±2 nm, 540±2 nm,560±2 nm, 580±2 nm, 590±2 nm, 610±2 nm, 620±2 nm, and 640±2 nm; or (iii)500±2 nm, 530±2 nm, 545±2 nm, 570±2 nm, 585±2 nm, 600±2 nm, 615±2 nm,and 640±2 nm.
 10. The method of claim 1, wherein the predetermined setof eight to twelve spectral bands consists of a set of eight spectralbands, wherein the set of eight spectral bands have central wavelengthsof: (i) 510±1 nm, 530±1 nm, 540±1 nm, 560±1 nm, 580±1 nm, 590±1 nm,620±1 nm, and 660±1 nm; (ii) 520±1 nm, 540±1 nm, 560±1 nm, 580±1 nm,590±1 nm, 610±1 nm, 620±1 nm, and 640±1 nm; or (iii) 500±1 nm, 530±1 nm,545±1 nm, 570±1 nm, 585±1 nm, 600±1 nm, 615±1 nm, and 640±1 nm.
 11. Themethod of claim 1, wherein the predetermined set of eight to twelvespectral bands consists of a set of eight spectral bands, where the setof eight spectral bands have central wavelengths of: (i) 510 nm, 530 nm,540 nm, 560 nm, 580 nm, 590 nm, 620 nm, and 660 nm; (ii) 520 nm, 540 nm,560 nm, 580 nm, 590 nm, 610 nm, 620 nm, and 640 nm; or (iii) 500 nm, 530nm, 545 nm, 570 nm, 585 nm, 600 nm, 615 nm, and 640 nm.
 12. The methodof claim 1, wherein each respective spectral band in the eight to tenspectral bands has a full width at half maximum of less than 5 nm. 13.An electronic device, comprising: one or more processors; memory; one ormore programs, the one or more programs stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: obtaining a data set comprising aplurality of images of a tissue of interest, each respective image inthe plurality of images resolved at a different spectral band in apredetermined set of eight to twelve spectral bands, and comprising anarray of pixel values; registering, using the processor, the pluralityof images on a pixel-by-pixel basis, to form a plurality of registeredimages of the tissue; and performing spectral analysis at a plurality ofpoints in a two-dimensional area of the plurality of registered imagesof the tissue, the spectral analysis including determining approximatevalues of oxyhemoglobin levels and deoxyhemoglobin levels at eachrespective point in the plurality of points, wherein the predeterminedset of eight to twelve spectral bands includes spectral bands havingcentral wavelengths of: (i) 510±3 nm, 530±3 nm, 540±3 nm, 560±3 nm,580±3 nm, 590±3 nm, 620±3 nm, and 660±3 nm; (ii) 520±3 nm, 540±3 nm,560±3 nm, 580±3 nm, 590±3 nm, 610±3 nm, 620±3 nm, and 640±3 nm; or (iii)500±3 nm, 530±3 nm, 545±3 nm, 570±3 nm, 585±3 nm, 600±3 nm, 615±3 nm,and 640±3 nm; and wherein each respective spectral band in the eight totwelve spectral bands has a full width at half maximum of less than 15nm.
 14. The electronic device of claim 13, wherein the electronic deviceis an imaging system further comprising: one or more photo-sensors, theone or more photo-sensors in electronic communication with the one ormore processors and configured to resolve light of the predetermined setof eight to twelve spectral bands, and wherein the instructions forobtaining the data set include instructions for capturing the pluralityof images of the tissue of interest using the one or more photo-sensors.15. The electronic device of claim 14, wherein the plurality of imagesis captured concurrently.
 16. The electronic device of claim 14, whereina first subset of the plurality of images is captured concurrently at afirst time point and a second subset of the plurality of images iscaptured concurrently at a second time point, other than the first timepoint.
 17. The electronic device of claim 13, wherein the instructionsfor performing the spectral analysis includes instructions for:resolving absorption signals at each respective point in the pluralityof points; accounting for a melanin contribution and loss of signal fromdiffuse scattering at each respective point in the plurality of points,thereby forming a plurality of corrected absorption signals; anddetermining approximate values of oxyhemoglobin levels anddeoxyhemoglobin levels from the corrected absorption signals at eachrespective point in the plurality of points.
 18. The electronic deviceof claim 17, wherein the contribution provided by melanin and the lossesprovided by diffuse scattering to the plurality of tissue oxygenationmeasurements are collectively modeled as a second order polynomial. 19.The electronic device of claim 13, wherein the predetermined set ofeight to twelve spectral bands consists of a set of eight spectralbands, wherein the set of eight spectral bands have central wavelengthsof: (i) 510±3 nm, 530±3 nm, 540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm,620±3 nm, and 660±3 nm; (ii) 520±3 nm, 540±3 nm, 560±3 nm, 580±3 nm,590±3 nm, 610±3 nm, 620±3 nm, and 640±3 nm; or (iii) 500±3 nm, 530±3 nm,545±3 nm, 570±3 nm, 585±3 nm, 600±3 nm, 615±3 nm, and 640±3 nm.
 20. Theelectronic device of claim 13, wherein the predetermined set of eight totwelve spectral bands consists of a set of eight spectral bands, whereinthe set of eight spectral bands have central wavelengths of: (i) 510 nm,530 nm, 540 nm, 560 nm, 580 nm, 590 nm, 620 nm, and 660 nm; (ii) 520 nm,540 nm, 560 nm, 580 nm, 590 nm, 610 nm, 620 nm, and 640 nm; or (iii) 500nm, 530 nm, 545 nm, 570 nm, 585 nm, 600 nm, 615 nm, and 640 nm.
 21. Theelectronic device of claim 13, wherein each respective spectral band inthe eight to twelve spectral bands has a full width at half maximum ofless than 5 nm.
 22. A nontransitory computer-readable storage mediumstoring one or more programs, the one or more programs comprisinginstructions, which when executed by an electronic device comprising aprocessor and memory, cause the electronic device to: obtain a data setcomprising a plurality of images of a tissue of interest, eachrespective image in the plurality of images resolved at a differentspectral band, in a predetermined set of eight to twelve spectral bands,and comprising an array of pixel values; register, using the processor,the plurality of images on a pixel-by-pixel basis, to form a pluralityof registered images of the tissue; and perform spectral analysis at aplurality of points in a two-dimensional area of the plurality ofregistered images of the tissue, the spectral analysis includingdetermining approximate values of oxyhemoglobin levels anddeoxyhemoglobin levels at each respective point in the plurality ofpoints, wherein the predetermined set of eight to twelve spectral bandsincludes spectral bands having central wavelengths of: (i) 510±3 nm,530±3 nm, 540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm, 620±3 nm, and 660±3nm; (ii) 520±3 nm, 540±3 nm, 560±3 nm, 580±3 nm, 590±3 nm, 610±3 nm,620±3 nm, and 640±3 nm; or (iii) 500±3 nm, 530±3 nm, 545±3 nm, 570±3 nm,585±3 nm, 600±3 nm, 615±3 nm, and 640±3 nm; and wherein each respectivespectral band in the eight to ten spectral bands has a full width athalf maximum of less than 10 nm.
 23. The nontransitory computer-readablestorage medium of claim 22, wherein the instructions, when executed byan imaging system further comprising one or more photo-sensorsconfigured to resolve light of the predetermined set of eight to twelvespectral bands, further cause the imaging system to: capture theplurality of images of the tissue of interest, each respective image inthe plurality of images resolved at a different spectral band in thepredetermined set of eight to twelve spectral bands, thereby obtainingthe plurality of images of the tissue of interest.
 24. Thecomputer-readable storage medium of claim 23, wherein the instructionscause the imaging system to capture the plurality of imagesconcurrently.
 25. The computer-readable storage medium of claim 23,wherein the instructions cause the imaging system to capture a firstsubset of the plurality of images concurrently at a first time point andto capture a second subset of the plurality images concurrently at asecond time point, other than the first time point.
 26. Thecomputer-readable storage medium according to claim 22, wherein theinstructions for performing the spectral analysis cause the electronicdevice to: resolve absorption signals at each respective point in theplurality of points; account for a melanin contribution and loss ofsignal from diffuse scattering at each respective point in the pluralityof points, to form a plurality of corrected absorption signals; anddetermine approximate values of oxyhemoglobin levels and deoxyhemoglobinlevels from the corrected absorption signals at each respective point inthe plurality of points.
 27. The computer-readable storage medium ofclaim 26, wherein the contribution provided by melanin and the lossesprovided by diffuse scattering to the plurality of tissue oxygenationmeasurements are collectively modeled as a second order polynomial. 28.The computer-readable storage medium according to claim 22, wherein thepredetermined set of eight to twelve spectral bands consists of a set ofeight spectral bands, wherein the set of eight spectral bands havecentral wavelengths of: (i) 510±3 nm, 530±3 nm, 540±3 nm, 560±3 nm,580±3 nm, 590±3 nm, 620±3 nm, and 660±3 nm; (ii) 520±3 nm, 540±3 nm,560±3 nm, 580±3 nm, 590±3 nm, 610±3 nm, 620±3 nm, and 640±3 nm; or (iii)500±3 nm, 530±3 nm, 545±3 nm, 570±3 nm, 585±3 nm, 600±3 nm, 615±3 nm,and 640±3 nm.
 29. The computer-readable storage medium according toclaim 22, wherein the predetermined set of eight to twelve spectralbands consists of a set of eight spectral bands, wherein the set ofeight spectral bands have central wavelengths of: (i) 510 nm, 530 nm,540 nm, 560 nm, 580 nm, 590 nm, 620 nm, and 660 nm; (ii) 520 nm, 540 nm,560 nm, 580 nm, 590 nm, 610 nm, 620 nm, and 640 nm; or (iii) 500 nm, 530nm, 545 nm, 570 nm, 585 nm, 600 nm, 615 nm, and 640 nm.
 30. Thecomputer-readable storage medium according to claim 22, wherein eachrespective spectral band in the eight to twelve spectral bands has afull width at half maximum of less than 5 nm.